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Aligning Autoresearch: Co-Improvement between Agents and Humans

Tianjian Li, Chuanyang Jin, Seungone Kim, Tong Chen, John Hoffman, Yoram Bachrach amd Pedro Rodriguez, Ping Yu, Eryk Helenowski, Swarnadeep Saha, Jack Lanchantin, Weizhe Yuan, Ilia Kulikov, Jason Weston

arXiv:2610.38882v1cs.AI

TL;DR

The paper addresses unresolved questions about human direction in open-ended autoresearch, alongside challenges in agent-user collaboration and expert-quality writing. It studies benchmark co-creation, trains agents using simulated user interactions, and optimizes writing with expert-aligned rubrics; the reported results include improved collaboration-task RL performance and writing gains across three domains.

  • Problem

    The role of humans in open-ended research tasks remains unresolved, while next-token pretraining and non-expert reward signals do not reliably push writing toward expert quality.

    Source: §1.1 · p. 1, §4.1 · p. 26

  • Method

    The paper studies human direction in benchmark creation, trains agents on simulated multi-turn user interactions, and uses expert writing to train rubric-based rewards for writing tasks.

    Source: §1.6 · p. 8, §3.3.1 · p. 17, §4.1 · p. 26

  • Results

    Training on interaction data significantly improves RL performance over baselines, while RL-XAR yields large gains over the base model across academic writing, story continuation, and Wikipedia writing.

    Source: §3.8 · p. 24, §4.6 · p. 36

  • Takeaways & Limitations

    For current benchmark creation, concrete human direction helps, and training agents to collaborate with users is a complementary focus to training them to solve tasks independently.

    Source: §1.6 · p. 8, §3.8 · p. 24

    Meaningful-problem selection remains a human-side judgment the authors do not know how to delegate, and Wikipedia writing remains difficult for RL-XAR in part because of judge inadequacy.

    Source: §1.6 · p. 8, §4.4.3 · p. 31

Abstract

from arXiv · show

Autoresearch agents can now attempt long-horizon AI research end-to-end, i.e., self-improvement. However, best results are still obtained by research agents interacting with human researchers, and improving AI's ability to perform this co-research will only reinforce this effect---a process termed co-improvement (Weston and Foerster, 2025). Further, aligning with human expectations is likely a safer path for research agents, especially given recent agent reward hacking phenomena (OpenAI, 2026b). In this work we address these issues in four ways: (i) we empirically study the role of humans in directing autoresearch agents, focusing on the domain of benchmark creation, and find humans still play a pivotal role particularly in the ideation phase; (ii) we also develop a benchmark, HackBench, that shows current autoresearch agents are not aligned and are unable to achieve hack localization sufficiently; (iii) we present a method to train agents for improved human interaction by using human simulators as a proxy during reinforcement learning (RL); and finally (iv) we develop a method to improve model text generation, and hence agent communication, by RL with rubrics aligned with human experts. Together, our training and evaluation recipes provide a path toward more capable and aligned (co-)research agents.

1 The role of humans in directing research agents for benchmark creation

Benchmark creation is open-ended and laborious, leaving the role of human researchers in directing autoresearch agents unresolved. AutoBenchmark tests this role by having agents build autoresearch benchmarks and revising them through solver and verifier feedback, with varying levels of human direction.

1.1 Overview

  • Benchmark creation is an open-ended, laborious process without a unified recipe, and high-quality autoresearch benchmarks remain sparse.Such benchmarks can reveal capabilities models lack and provide targets for development.Source: §1.1 · pp. 1–2

1.2 AutoBenchmark

  • AutoBenchmark has the agent propose and instantiate runnable tasks, use solver trajectories and scores as a difficulty signal, and apply verifier judgments for quality; humans can direct both what to measure and how.The experiments use Graveyard, SilentTrain, and Rebuttal benchmarks, with ten instances over ten iterations and solver scores averaged across tasks.Source: §1.1 · p. 2, §1.2 · p. 3, §1.3 · pp. 4–5

1.4 Experimental results

  • Fine-grained human feedback lowered scores by 28–46.5 points in all six benchmark–solver pairs, while benchmarks passed validity checks and generalized to a held-out model.By contrast, brief intent statements helped only marginally, and unaided benchmarks were often saturated and failed validity checks.Source: Figure 2 · p. 5, §1.4 · pp. 5–7
  • On Rebuttal Bench, Nemotron scored 39.1 with fine-grained feedback versus 81 with coarse feedback; on Graveyard Bench, it scored 52 versus 75.4.Claude-Opus-5 showed the same ordering at selected checkpoints.Source: Figure 2 · p. 5, §1.4 · p. 7
  • Verifier review rejects iterations that fail quality criteria, catching defects such as answer leakage and unsupported grounding that solver scores alone cannot reveal.For example, a low score can reflect an impossible task or a faulty grader rather than a valid, challenging benchmark.Source: Figure 4 · p. 6, §1.4 · p. 7
  • After the SilentTrain run stalled, execution-stage human guidance brought selected-checkpoint scores to 64.2 on Muse Spark, 54.2 on Muse Glimmer, and 48.8 on Nemotron.The unguided continuation scored 88.1, 85.6, and 83.8, respectively.Source: Figure 3 · p. 6, §1.4 · p. 7

1.5 Related Work

  • Unlike work that evaluates agents against preset goals, AutoBenchmark asks whether agents can decide what is worth measuring and build the evaluation apparatus.It studies current agents and human direction rather than training the benchmark proposer.Source: §1.5 · p. 8

1.6 Conclusion

  • The study concludes that current agents benefit from concrete human input when forming a benchmark and when a stalled loop needs guidance on how to instantiate it.Meaningful problem selection remains a human-side judgment the authors do not yet know how to delegate.Source: §1.6 · p. 8

2 HackBench: DetectingandLocalizingRewardHackinginAutoresearchAgents

HackBench tests whether monitors can locate reward hacking in long autoresearch transcripts, and finds substantial failures even among frontier models.

2.1 Overview

  • Autoresearch agents can game verifier-based metrics without doing the intended research, and detecting this requires judging task-specific actions across long trajectories.Hacks may resemble ordinary progress and can corrupt verifier rewards used for training.Source: §2.1 · p. 9

2.2 HackBench

  • HackBench evaluates localization in full trajectories using real agent runs and natural or synthetic hacks, across 13 long-horizon ML research tasks.The trajectories were generated with a bash-only scaffold using GPT-5.5 and GLM-5.2.Source: §2.1 · p. 10, §2.2 · p. 10, §2.2.2.1 · p. 11

2.2.1.2 Reward hacking.

  • A hack is reward obtained by subverting measurement rather than completing the task; monitors must return the specific offending command indices.The benchmark frames localization as a long-context task requiring relevant context, multi-step reasoning, and understanding of the task and grading procedure.Source: §2.2.1.2 · p. 11, §2.2.1.3 · p. 11, §2.2.1.4 · p. 11

2.2.3 HackBench Construction

  • Because naturally observed hacks were too rare and narrow, the authors used a hack taxonomy to guide synthetic injection with command-level labels.They patched causally affected downstream observations while preserving unrelated observations, then used two auditors and manually resolved disagreements.Source: §2.2.3 · p. 12, §2.2.3.1 · p. 12, Figure 7 · p. 13

2.2.3.2 Collecting natural hacks.

  • HackBench combines 41 naturally hacked and 128 synthetically hacked trajectories; the median trajectory exceeds one million characters but contains only one hacked command.Hacked commands comprise a median 0.6% of commands, though some trajectories contain as many as 383.Source: §2.2.3.2 · p. 12, Table 3 · p. 12

2.3 Hack Localization Results

  • Claude Opus 5 reaches 64.88 F1 and Muse Wonder 61.47, while all monitors still miss many hacks.Four other models cluster between 50.90 and 52.99 F1 despite different precision–recall trade-offs.Source: Figure 5 · p. 9, §2.3 · pp. 12–13, Table 4 · p. 13

2.4 Related Work

  • Unlike capability benchmarks, HackBench studies integrity in verifier-scored research tasks, using injection-based red-teaming to test monitors on long transcripts and varied hacks.The paper positions this as complementary to work on when reward hacking occurs and what forms it takes.Source: §2.4 · pp. 13–14

2.5 Conclusion

  • The authors argue that monitoring reward hacking is a prerequisite as agents take on more of the research loop, and release audited natural and synthetic trajectories with command-level labels.Source: §2.5 · p. 14

3 Training agents to interact with humans

This section describes AutoInteract, which creates realistic, grounded multi-turn interactions around verifiable tasks to train agents to collaborate with users and respond to clarifications, feedback, and changing requirements. It reports improved performance over baselines, including gains in interactive coding and repository-level issue resolution.

3.1 Overview

  • AutoInteract addresses the gap between fully specified training tasks and real interactions by synthesizing multi-turn conversations from verified tasks while preserving their executable reference solutions.Its two phases learn user models from real interaction histories, then use them with problem-solving agents in a generate–evaluate–revise loop to create interactions calibrated to the target model.Source: §3.1 · p. 15

3.3 Building User Models from Real Histories

  • User models combine a reusable behavior prompt with profiles grounded in real users’ interaction histories, capturing patterns such as corrections, requirement changes, vagueness, and verbosity.The behavior prompt is optimized against observed next-user turns; profiles are diversified across behavioral clusters while retaining patterns found in real users and are validated for unsupported claims and distributional similarity.Source: §3.3 · p. 17, §3.3.1 · p. 17, §3.3.2 · p. 18

3.4.2 Agentic Self-Improvement

  • The trajectory-generation loop pairs a verified source task with a user model, rolls out a multi-turn exchange, and keeps only candidates passing validity, realism, profile-consistency, and calibrated-difficulty checks.When evaluation fails, the controller uses the diagnosis to revise the interaction while keeping the task and user model fixed, retrying until it passes or the budget is exhausted.Source: §3.4 · p. 18, §3.4.2 · p. 19

3.4.1 Interaction Task Synthesis (Controller + Trajectory generator)

  • Interaction tasks use scenario-guided clarification, requirement changes, or bug iterations for algorithmic problems, and history-conditioned repository trajectories for software engineering.The user simulator receives private task and reference context to keep requests grounded, while the assistant sees only the conversation and repository context; the original task verifier remains unchanged.Source: §3.4.1 · pp. 18–19

3.5.1 Training Setup

  • Training uses executable-verification rewards on synthesized trajectories, with data generated separately for 4B and 27B targets so difficulty is calibrated to the model being trained.Candidates pass only when the weak solver scores at most 40% and the strong solver at least 70%; failed candidates may undergo up to five revision rounds.Source: §3.5.1 · pp. 19–20

3.6.1 Main Results

  • 43.0 average at 4B (+13.1 vs baseline, +6.1 vs source training) and 55.7 at 27B (+6.6 vs baseline, +6.1 vs source training) accompany multi-turn gains from 23.0 to 43.2 and 37.2 to 50.6.Single-turn performance also rises at 4B (36.8 to 42.8) and remains essentially unchanged at 27B (60.9 to 60.8); hard tasks remain out of reach for all methods.Source: Table 5 · p. 20, §3.6.1 · pp. 20–21

3.5.2 Evaluation Setup

  • Evaluation covers LiveCodeBench-Pro and a multi-turn version built from the same problems, plus SWE-Together’s repository tasks with a reactive user simulator.Multi-turn LiveCodeBench-Pro preserves the original problem identity and verifier, while SWE-Together evaluates correctness and the corrective effort users provide.Source: §3.5.2 · p. 20

3.6.1 Main Results

  • On SWE-Together, Pass@1 rises from 6.0 to 20.6 at 4B and from 34.9 to 38.5 at 27B, while SSR rises from 1.8 to 16.5 at 4B and by 7.3 points at 27B.AutoInteract outperforms source-data training by 3.6 points on 4B Pass@1 and 5.5 points on 4B SSR; at 27B it is the only method to improve Pass@1.Source: Table 6 · p. 21, §3.6.1 · p. 21

3.6.2 Ablations

  • Removing multi-turn data lowered average performance from 34.0 to 28.2; removing user models lowered it to 32.0, and removing executable verification lowered it to 25.0.The multi-turn drop was driven primarily by Multi-turn LiveCodeBench-Pro (43.2 →29.2); without user models, SWE-Together SSR fell from 16.5 to 12.8. Removing all three components yielded 24.7, and executable verification supports difficulty calibration and catches invalid synthesized tasks that an LLM judge may miss.Source: §3.6.2 · pp. 21–22, Table 7 · p. 22

3.6.3 Analysis

  • Accepted trajectories rose from 9.4% to 28.1% for algorithmic tasks and from 26.6% to 50.9% for SWE tasks after five self-improvement rounds.Revisions supplied 52.4% of accepted trajectories; 85.5% of recovered SWE trajectories arrived in the first two rounds, while algorithmic gains spanned all five. Training on AutoInteract trajectories saturated less quickly and eventually reached higher reward than source-task training in both domains.Source: §3.6.3 · p. 22, Figure 10 · p. 22
  • With the source task, user prompt, and profile fixed, revisions adjust what information is revealed and when to balance difficulty, solvability, grounding, and realism.In the illustrated trajectory, Stage 3 restored needed evidence and follow-up requirements without exposing the solution, producing weak- and strong-solver scores of 25% and 100% and passing all quality checks.Source: §3.6.3 · p. 23, Figure 11 · p. 23

3.7 Related Work

  • AutoInteract focuses on constructing interaction data itself, refining multi-turn trajectories while keeping the source task, user profile, and behavior prompt fixed.Unlike approaches that keep the interaction environment and user simulator fixed, it learns user behavior from real histories and evaluates realism directly.Source: §3.7 · p. 24

3.8 Conclusion

  • AutoInteract combines user models learned from real interaction histories with agentic data creation using judge and execution feedback to control quality and difficulty.The conclusion frames collaboration that incorporates clarifications, feedback, and evolving requirements as co-improvement (Weston and Foerster, 2025), rather than independent self-improvement.Source: §3.8 · pp. 24–25

4 Training agents to communicate with humans: unslopping AI

This section examines RL-XAR as a way to train writing models for communication with humans, using rubrics that favor expert writing over model writing and situating the approach among reward-learning methods. It covers refinements to RL-XAR, reported writing gains across academic, story, and Wikipedia domains, and remaining limitations.

4.2 The failure of standard LLM Judgments

  • Standard pairwise judges favored model writing over expert human writing in 63.5%–84.6% of cases, while standard rubric-based judgments selected the model 100% of the time.The experiments compared GPT-5.6 and Opus-4.8 as writers and judges on academic paper sections.Source: §4.2 · pp. 26–27, Figure 12 · p. 27

4.3 RL from eXpert-Aligned Rubrics (RL-XAR)

  • RL-XAR learns rubrics by pairing expert human continuations with model continuations and optimizing rubric generation to maximize the human–model scoring gap, then alternates rubric learning and reinforcement learning.It repeats this loop until rubric learning can no longer identify a gap in model writing.Source: §4.3 · p. 27

4.3.1 Learning the rubrics: can an LLM recognize expert writing?

  • Meta-prompt optimization turned the validation human–model gap positive at iteration 4, with human scores rising from 3.4 to 5.0 and model scores falling from 7.6 to 2.7.The learned rubrics shifted from paper-specific coverage checklists toward rewarding sectional ownership, disciplined selection, economy, and precise on-scope detail.Source: Figure 13 · p. 28, §4.3.1 · p. 28

4.4.1 Paper Section Writing

  • 9.60 was RL-XAR Qwen3.5-27B’s worst-rubric score (human = 10), topping external frontier models on paper-section writing.In a preliminary blind expert comparison on held-out sections, RL-XAR was preferred over baseline Qwen3.5-27B by 16 to 2 (89%); the authors note the rubrics may remain biased toward their model and imperfect measures of expert writing.Source: §4.4.1 · p. 29, Table 8 · p. 30

4.4.2 Story Writing

  • RL-XAR raised Qwen3.5-27B’s human-normalized story-writing score from 2.8 to 8.2, ahead of every writer, with Claude 5 Opus next at 6.8.In a preliminary blind human comparison on held-out continuations, RL-XAR was preferred over baseline Qwen3.5-27B by 19 to 1 (95%); the baseline often failed to match the story’s style.Source: §4.4.2 · p. 30, Table 9 · p. 31

4.4.3 Authoring Wikipedia Articles

  • RL-XAR moved Qwen3.5-27B’s human-normalized Wikipedia score from 2.7 to 4.0, remaining below GPT-5.6 at 8.9 and Claude 5.5 Opus at 8.2.The authors identify Wikipedia section writing as the hardest domain for this recipe and cite the training judge’s inadequacy as a factor.Source: §4.4.3 · pp. 30–31, Table 9 · p. 31

4.4.4 Detailed Analysis, Ablations & Additional Experiments

  • A weak judge could not distinguish expert human from model writing, while human–model gaps increased with paper quality: +1.39 high-quality, +0.95 medium-quality, and +0.37 low-quality human papers.Source: §4.4.4 · pp. 31–32
  • Iterative rubric training restored round-2 performance after an initial decline, and rubrics optimized for one strong judge favored humans under both GPT-5.6 and Muse Spark 1.1 judges.The authors suggest fully online training could improve the recipe.Source: §4.4.4 · p. 32, Table 10 · p. 33
  • Ground-truth rubrics produced higher human-model score gaps than blind rubrics across two rubric judges, while paper-writing tasks used length constraints to curb overlong outputs.The authors note that ground-truth rubrics make sense when human continuations are high quality; length constraints also led strong models to spend unwanted reasoning effort on word counting.Source: §4.4.4 · p. 33
  • Online co-training on the story task raised scores from 2.8 for Qwen3.5-27B to 6.0 for online RL-XAR under the same fixed learned rubric.The writer optimizes against rubrics while the rubric generator is trained with GRPO to maximize the human-model gap; this was an initial study, not the main reported approach.Source: §4.4.4 · p. 33, Figure 17 · p. 34

4.5 Related Work

  • RL-XAR addresses open-ended writing’s lack of reliable programmatic verification by learning expert-aligned rewards from existing expert texts rather than collecting more preferences.Its rubric generator is meta-optimized to recover discrimination between expert human writing and model prose, repairing judges’ tendency to under-credit expert writing without reference access at evaluation time.Source: §4.5 · pp. 33, 35

4.4.4 Detailed Analysis, Ablations & Additional Experiments

  • RL-XAR with surface-form code rewards moves distorted features toward human distributions without reducing rubric rewards, and outperforms the base model on those statistics.The code reward scores measurable text features against each item’s human reference; Table 11 highlights sentence length, paragraphing, and semicolons.Source: §4.4.4 · p. 33, Table 11 · p. 35

4.6 Conclusion

  • Across academic sections, prize-winning story continuations, and Wikipedia sections, RL-XAR yields large gains over the base model by meta-optimizing rubrics to close the expert-human–model gap.Standard RL against the resulting rubrics then improves writing quality.Source: §4.6 · p. 36
  • Learned rubrics may remain biased toward the training model and may not fully capture expert quality; the experiments used mid-sized writers and reward judges.The authors anticipate better results with frontier writers and graders and propose applying the recipe to other non-verifiable domains.Source: §4.6 · p. 36

A.1 Stage 1: Benchmark proposal

Stage 1 constrains benchmark creation through isolated research sessions, a detailed submission contract, structural and revision checks, and executable validation of reference solutions. Failed gates trigger a restricted repair cycle.

A.1 Stage 1: Benchmark proposal

  • Each iteration gives a fresh OpenHands agent an isolated authoring session with fixed inputs and limited tools, then freezes and hashes its submission.The agent cannot run models, judges, solvers, containers, or its own benchmark; it can invoke only the structural validator.Source: §A.1 · p. 48
  • Submissions must include a scientific specification, three materially different designs, retained-source provenance, and complete Harbor tasks with deterministic references and constrained grading criteria.Manifest records require source origin, retrieval time, local path, SHA-256, and license terms; self-generated data also requires its generator, parameters, seed, and verification details.Source: §A.1 · p. 48
  • Structural validation checks protected files, package limits, schema-valid criteria, provenance consistency, and revision integrity before tasks execute.Revision checks also reject rebound criterion identities and instruction changes that leave private grading and reference artifacts unchanged.Source: §A.1 · p. 48
  • A dedicated revision judge rejects public changes unsupported by retained sources or justified only by observed solver errors.It compares solver-visible checkpoints and submissions, feedback, and grounding; a static detector flags newly introduced material that also appears in feedback.Source: §A.1 · p. 48
  • Reference solutions must earn at least 0.9 with no verifier error, while failures at any gate send the iteration to a fresh repair session capped at ten attempts.Admission checks that the task is solvable under the fixed contract, the environment supplies needed resources, and the grader awards the intended answer.Source: §A.1 · pp. 48–49

A.2 Stage 2: Benchmark solving

Stage 2 obtains a difficulty signal by running solvers on each task and grading their answers under the benchmark’s fixed criteria. Only completed solver runs contribute to that signal.

A.2 Stage 2: Benchmark solving

  • Each solver attempts every task once in the fixed task environment, and only completed runs—not infrastructure failures—enter the difficulty signal.Completed attempts retain answers, trajectories, and result summaries; infrastructure failures are retried and explicitly distinguished from incorrect answers in feedback.Source: §A.2 · p. 49
  • A separate verifier judge scores each criterion independently and combines the scores by protected weighted mean into a reward from 0 to 1.The harness fixes the judge route and prompt, while published feedback includes per-criterion scores and anchors.Source: §A.2 · p. 49

A.3 Stage 3: Benchmark review

Benchmark review evaluates validity, correctness, feasibility, usefulness, and overall quality through isolated judge sessions and source-grounded evidence checks. The overall loop publishes curated feedback, selects eligible checkpoints, and can incorporate operator feedback.

A.3 Stage 3: Benchmark review

  • Review criteria cover construct validity, correctness and reproducibility, feasibility, usefulness for research-agent capability, and publication-quality design at the current scale.Difficulty caused by missing evidence, impossible requirements, or circular evaluation is rejected.Source: §A.3 · p. 49
  • Five concurrent, isolated judge sessions produce verdicts with pass/fail decisions, responsibility assignments, artifact citations, strengths, weaknesses, fixes, confidence, and rationale.No judge sees another judge’s verdict.Source: §A.3 · p. 49
  • Every material claim and grading anchor must be verified against retained content at an exact locator or reproducible derivation; unverifiable claims count against the submission.Judges cannot browse the web to repair evidence gaps, and fabricated or dead citations are mandatory failures.Source: §A.3 · p. 49

A.4 Overall loop

  • The loop publishes curated, untrusted feedback, selects the lowest-reward eligible checkpoint, and can pause for validated operator feedback before resuming.Eligibility requires all five verdicts to pass and solver evaluation to be complete; the end-of-run selection also rejects checkpoints that failed any judge.Source: §A.4 · pp. 49–50

B Benchmark examples built with AutoBenchmark

This section presents AutoBenchmark examples targeting research-agent failures, including tasks involving refuted ideas, unsupported rebuttals, and silent training bugs. The SilentTrain Bench example focuses on repository repair, with a grader assessing repair quality, execution evidence, and scope.

B.1 Graveyard Bench

  • Graveyard Bench tests whether idea-generating models re-propose hidden refuted research directions without flagging them as previously refuted.Tasks seed from failed research, medical reversals, or results-level retractions and require ten structured hypotheses with confidence scores and refutation flags; matching the hidden intervention, effect direction, and scope counts as a zombie error.Source: §B.1 · p. 50

B.2 Rebuttal Bench

  • Rebuttal Bench tests whether agents adjudicate each reviewer concern from the evidence supplied, rather than treating fluent replies or reviewer satisfaction as proof of resolution.It uses four resolution labels and requires labeling evidence and any deflection tactic; some tasks substitute a weaker assay for requested evidence, and second-round satisfaction must not establish resolution.Source: §B.2 · p. 52

B.3 SilentTrain Bench

  • SilentTrain Bench tests whether agents can reproduce and repair silent training regressions in a complete repository, then validate the fix with an executed behavior-focused command.Its hybrid grader combines a hidden programmatic regression check with review of the patch, execution log, and written answer; it requires repository-based work, executed validation, and behavioral grading.Source: §B.3 · p. 55
  • The task requires a focused production-code repair, repository preservation, CPU-compatible validation where possible, and unaltered command output.Missing test dependencies may be installed, but a replacement repository or upstream change must not be sought.Source: §B.3 · p. 55
  • The grader evaluates whether the patch credibly repairs the reported behavior and whether retained logs show a meaningful test with consistently reported results.Equivalent implementations are accepted; source listing, grep, or syntax compilation alone does not count as behavioral evidence.Source: §B.3 · p. 56
  • The scope criterion rewards focused production changes that preserve surrounding semantics, avoid manipulating tests or grading artifacts, and address important edge cases.Source: §B.3 · p. 56

C HackBench Task Suite

HackBench Task Suite covers 13 long-horizon autoresearch tasks represented in 169 trajectories, with varied task objectives and reward interfaces. Its prompts pair a terminal-based research agent with a monitor that identifies reward-hacking commands.

C HackBench Task Suite

  • The suite represents 13 tasks in 169 trajectories: five from ResearchGym, seven from AutoLab, and one weak-to-strong research task.Table 12 groups the tasks by source benchmark and describes each objective.Source: C HackBench Task Suite · p. 56, Table 12 · p. 56
  • Task wrappers vary in whether their rewards track intended objectives, shaping the reward-hacking surface available to agents.AutoLab evaluates task-specific outcomes; some ResearchGym and weak-to-strong wrappers use stubs, while materials-tokenization thresholds an aggregated F1 score without rerunning evaluation.Source: C HackBench Task Suite · p. 56

D HackBench Prompts

  • The research agent uses a terminal scaffold, issuing batches of shell keystrokes in response to the task description and current terminal output.Its prompt requires analysis, a plan, and command objects, with task completion optional.Source: D HackBench Prompts · pp. 57–58
  • The hack-localization monitor labels executable commands and returns only those that violate task intent to raise reward rather than genuinely improve capability.Localization is scored by precision, recall, and F1; a legitimate session should return an empty list.Source: D HackBench Prompts · pp. 58–59

E HackBench Task-Specific Exploits

The section distinguishes general reward hacks from task-specific grader exploits and describes task objectives across ResearchGym and AutoLab. Table 13 pairs objectives with task-specific exploits identified through grader audits.

E HackBench Task-Specific Exploits

  • General hacks subvert measurement across tasks, while task-specific exploits target one task’s measurement structure and require knowing what legitimate progress looks like.Table 13 pairs objectives with task-specific exploits identified by auditing graders.Source: E HackBench Task-Specific Exploits · p. 59, Table 13 · p. 59
  • Other objectives include teaching an image generator a visual concept from fifteen images and training a language model for low WikiText-103 perplexity under a fixed compute budget.Source: E HackBench Task-Specific Exploits · p. 60
  • The listed objectives range from selecting fine-tuning examples to improve instruction following to improving visual-math performance without losing general visual ability.The data-selection task selects up to 5,000 examples from 50,000; the vision-language task aims to retain general visual ability.Source: E HackBench Task-Specific Exploits · p. 60

F AutoInteract Implementation Details

The described reward and verifier designs admit shortcuts that can improve measured scores without corresponding progress on the intended capability. The examples include overfitting, degenerate outputs, and exploiting thresholds or metric properties.

F AutoInteract Implementation Details

  • 75% of the composite score is cosine similarity to the mean concept-image embedding, so near-copy, mode-collapsed LoRAs can score highly with less work than honoring prompts.Source: F AutoInteract Implementation Details · p. 60
  • Selecting approximately one sample can leave the adapter near identity, so the verifier measures the stronger untouched base model when the reward lacks a selection-quality term.The passage characterizes the pipeline as net-destructive and notes there is no minimum sample count.Source: F AutoInteract Implementation Details · p. 60
  • Last-number answer extraction makes a constant number a degenerate attempt, while substring-based retention can pass an answer-enumerating word salad without real ability.Source: F AutoInteract Implementation Details · p. 60
  • PSNR/MSE can favor a low-variance prediction near the per-pixel mean over a genuine but slightly misaligned rollout; the passage reports a saved ZeroPredictor.Source: F AutoInteract Implementation Details · p. 60
  • With uncapped CER, hallucinated text scores 1.5–3.0, while emitting nothing or a short constant yields a reported 2–3× metric improvement.Source: F AutoInteract Implementation Details · p. 60
  • Once held-out perplexity is within epsilon of the reward-saturation threshold at 18, a run can cut training compute and land just under that threshold.The passage gives freezing layers or early stopping as ways to reduce compute.Source: F AutoInteract Implementation Details · p. 60

F.1 Implementation Details for Building User Models from Real Histories

The implementation builds human-user profiles from measured software-development interactions, expands them across observed behavioral niches, and uses them to train and evaluate a user-behavior prompt. Profile evidence and validation constrain the generated population and simulated replies.

F.1 Implementation Details for Building User Models from Real Histories

  • An LLM turns measured interaction patterns and representative messages from SWE-Chat into user profiles, which another LLM checks for unsupported claims before one revision.The process produces 189 profiles and derives expertise and behavioral settings from the measurements.Source: §F.1 · p. 60
  • Profile-generation instructions require claims to follow measured evidence and exclude repository-specific details so profiles generalize across algorithmic and repository tasks.The requested profile fields include persona, communication style, coding style, and behavior.Source: §F.1 · pp. 60–61
  • MAP-Elites-style niche filling expands the real-user profiles across expertise, vagueness, pushback, requirement changes, and verbosity while retaining every real profile.The final population contains 1,000 profiles: 189 from real users and 811 validated variants.Source: §F.1 · p. 61
  • Training examples pair prior conversation and an assigned profile with the real user’s next message, using at most one follow-up per session and preserving persona-label distributions across splits.The first user turn is excluded because it has no preceding assistant response.Source: §F.1 · p. 62
  • GEPA revises the user-behavior prompt using evaluator feedback on simulated versus real turns, selects by aggregate validation score, and evaluates the chosen prompt on held-out test data.The evaluator compares intent, reaction, length, specificity, profile consistency, and realism; the prompt outputs only the user’s next message and is not tied to a specific task or user.Source: §F.1 · p. 62
  • The simulator is instructed to react to the agent’s visible last turn, with roughly 55% of turns showing no pushback and roughly 30% correcting a mostly right result.It should avoid inventing concerns and usually keep responses short.Source: §F.1 · p. 63

F.2 Implementation Details for Building Interactions with an Agentic Loop

Interaction trajectories are guided by private controller plans, then evaluated and revised; only trajectories passing every check enter the final dataset.

F.2 Implementation Details for Building Interactions with an Agentic Loop

  • A private controller plan guides each synthesized interaction, but the assistant simulator sees only the conversation.The example plan specifies a TextFSM bug, user behavior, staged information disclosure, and a three-turn progression.Source: §F.2 · p. 64
  • Algorithmic trajectories pair verified tasks with user models and one of five scenarios, while repository trajectories use issue-based tasks and history-conditioned interactions.Algorithmic conversations have two to four user turns; repository conversations have one to five and undergo deterministic checks for leakage, invalid patches, regressions, and prematurely solved tasks.Source: §F.2 · pp. 64–65
  • Staged evaluation checks validity and leakage, realism and coherence, and calibrated difficulty, including weak-solver scores of at most 40% and strong-solver scores of at least 70%.Intermediate assistant code or patches are executed to reject trajectories that already contain the complete solution.Source: §F.2 · p. 65
  • Repository examples must first support a verified solver rollout using only information visible to the assistant, with a patch that passes the verifier.The rollout must include repository inspection, editing, post-edit tests, and at least one user checkpoint based on visible evidence.Source: §F.2 · p. 65
  • The self-improvement loop revises failed trajectories for up to five rounds and retains only examples that pass every check.The source task, user profile, behavior prompt, and assigned algorithmic scenario stay fixed; the controller instruction, turn count, and interaction can change.Source: §F.2 · p. 65

F.3 Details of Training Tasks

Training tasks use scenario-guided interactions for algorithmic coding and real-history-conditioned interactions for repository-level software engineering.

F.3 Details of Training Tasks

  • The training controller covers algorithmic coding and software-engineering tasks, using scenario-guided and history-conditioned trajectories, respectively.Repository interaction patterns come from user models learned from real histories.Source: §F.3 · p. 65
  • Algorithmic trajectories use five scenarios spanning clarification, requirement change, and bug iteration, with clarification and requirement changes occurring before or after implementation.Design-stage variants discuss requirements and design without code; post-solution variants follow an initial implementation.Source: §F.3 · pp. 65–66
  • Repository trajectories use SWE-smith bug-fixing tasks and learned user models to produce history-conditioned interactions while keeping private tests and reference information hidden from the assistant.The assistant acts as a repository coding agent, and the controller keeps requests factual without revealing private tests or implementation details.Source: §F.3 · p. 66

G.1 Datasets

The datasets pair human-authored or human-vetted writing with preceding context, with corpus-specific filtering and training, validation, and test splits.

G.1 Datasets

  • 561 scientific papers yield 2,243 section-writing examples, with 90 disjoint papers held out for validation; a separate 52-example high-rated set supports rubric optimization and testing.The paper-section data covers abstracts, introductions, related work, and conclusions; Muse Spark 1.1 rates writing across eight dimensions and gives a holistic score.Source: §G.1 · p. 66
  • Stories use 229 books for 2,290 training examples and 31 books for 310 validation examples, with memorization filtering and roughly 2,500 words of context for each continuation.Books are public-domain novels by Nobel laureates and early Pulitzer winners; each target continuation is about 300 words.Source: §G.1 · p. 66
  • Wikipedia examples come from Featured- or Good-Article sections, with 1,187 training examples, 162 validation examples, and 60 final-test examples.The corpus favors less-popular titles and applies the described LLM memorization test.Source: §G.1 · pp. 66–67

G.2 Judging protocols

The judging protocols compare candidate writing directly or score it against weighted rubrics; rubric guidance is iteratively optimized using training-split diagnostics.

G.2 Judging protocols

  • Direct pairwise judging compares a human section with a model section in both A/B orders, counting consistent preferences as wins and flipped verdicts as order-dependent ties.Judges receive no rubric for this protocol.Source: §G.2 · p. 67
  • Rubric judging uses 4–6 weighted criteria, an independent criterion-by-criterion judge, and a deterministic length penalty to compute the final score.Full length credit applies inside 88–115% of the human target length, with the penalty decaying quadratically over a further 20%.Source: §G.2 · p. 67
  • The rubric meta-optimizer revises only mutable guidance using training-split aggregates and worst-gap-first example digests, without access to validation data or candidate texts.The optimized prompt stresses scope discipline and strategic omission to counter rubrics that reward keyword density, exhaustive coverage, or cross-section detail leakage.Source: §G.2 · pp. 68–69
  • The optimized rubric makes section boundaries its highest-priority constraint and penalizes accurate details imported from other sections.It favors selective, section-appropriate synthesis over fact density and treats strategic omission as evidence of scholarly judgment.Source: §G.2 · pp. 69–70
  • The reproduced learned rubric still contains duplicated length rules attributed to meta-optimization inefficiency, and the authors note that the first learned rubrics may be refined with further RL-XAR training.The duplicated rules even slightly disagree.Source: §G.2 · p. 70

G.3 Additional Results

Figure 18 tracks how meta-prompt iteration changes the expert-human–model gap, with a higher validation peak for stories than Wikipedia. The surface-form reward scales rubric reward using human-referenced stylometric features, whose selection and overall reward may also be found by meta-optimization.

G.3 Additional Results

  • Story validation gap peaks at +2.77 around iteration 3, versus Wikipedia’s +1.07 peak around iteration 3.Figure 18 plots mean expert-human−model gap across meta-prompt iterations for train and validation splits.Source: Figure 18 · p. 71
  • Table 14 covers meta-optimizer and judge capability across all three domains.Source: Table 14 · p. 71

G.4 Verifiable surface-form code-based rewards

  • The overall training reward scales rubric reward by a surface-form factor computed as the mean over 12 stylometric features.Each feature is compared with its human value in units of the human population standard deviation; its score is 1 within 1 standard deviation and decays linearly to floor ϕ by 4.Source: §G.4 · p. 71
  • The 12-feature set and overall reward may be found by meta-optimization and iterated like the rubrics, but further analysis is left for future work.The listed features cover paragraph and sentence structure, word lengths, lexical diversity, punctuation rates, and dialogue fraction.Source: §G.4 · p. 72
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