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InnovatorBench: Evaluating Agents' Ability to Conduct Innovative LLM Research

Yunze Wu, Dayuan Fu, Weiye Si, Zhen Huang, Mohan Jiang, Keyu Li, Shijie Xia, Jie Sun, Tianze Xu, Xiangkun Hu, Pengrui Lu, Xiaojie Cai, Lyumanshan Ye, Wenhong Zhu, Yang Xiao, Pengfei Liu

arXiv:2510.27598v2cs.AI

TL;DR

Existing benchmarks often test narrow skills in simplified settings, leaving the end-to-end research abilities of AI agents difficult to assess. The paper pairs InnovatorBench’s open-ended LLM research tasks with ResearchGym’s long-horizon research environment. Experiments report potential on code-based tasks lasting over 10 hours, alongside cases of impatience and resource mismanagement.

  • Problem

    Existing benchmarks often focus on narrow skills in simplified environments rather than assessing whether agents can orchestrate end-to-end research workflows.

    Source: §1 · p. 2

  • Method

    The paper introduces InnovatorBench, a 20-task benchmark for open-ended LLM research, and ResearchGym, an extensible environment for realistic long-horizon experiments.

    Source: §1 · p. 2

  • Results

    Agents show potential to handle code-based research tasks longer than 10 hours, while high research scores require comprehensive abilities such as creativity and code implementation.

    Source: §1 · p. 2, §5.4 · p. 8

  • Takeaways & Limitations

    InnovatorBench and ResearchGym provide a framework for assessing agents’ ability to carry out end-to-end LLM research workflows.

    Source: §1 · p. 2

    Case studies show impatience and resource mismanagement: agents terminated a training run prematurely and later launched training while inference was using the same machine.

    Source: §5.4 · p. 8

Abstract

from arXiv · show

AI agents could accelerate scientific discovery by automating hypothesis formation, experiment design, coding, execution, and analysis, yet existing benchmarks probe narrow skills in simplified settings. To address this gap, we introduce InnovatorBench, a benchmark-platform pair for realistic, end-to-end assessment of agents performing Large Language Model (LLM) research. It comprises 20 tasks spanning Data Construction, Filtering, Augmentation, Loss Design, Reward Design, and Scaffold Construction, which require runnable artifacts and assessment of correctness, performance, output quality, and uncertainty. To support agent operation, we develop ResearchGym, a research environment offering rich action spaces, distributed and long-horizon execution, asynchronous monitoring, and snapshot saving. We also implement a lightweight ReAct agent that couples explicit reasoning with executable planning using frontier models such as Claude-4, GPT-5, GLM-4.5, and Kimi-K2. Our experiments demonstrate that while frontier models show promise in code-driven research tasks, they struggle with fragile algorithm-related tasks and long-horizon decision making, such as impatience, poor resource management, and overreliance on template-based reasoning. Furthermore, agents require over 11 hours to achieve their best performance on InnovatorBench, underscoring the benchmark's difficulty and showing the potential of InnovatorBench to be the next generation of code-based research benchmark.

1. Introduction

Existing benchmarks often assess narrow skills in simplified settings, leaving open whether agents can orchestrate those skills into realistic, end-to-end research workflows. The paper introduces InnovatorBench and ResearchGym to evaluate that capability across LLM research tasks.

1. Introduction

  • The paper positions end-to-end orchestration as the key question for turning LLM capability gains into progress by AI research agents.AI researchers are described as agents integrating research stages, including insight generation and implementation.Source: §1 · p. 2
  • Existing benchmarks often focus on a single dimension or reproduction, while simplified environments limit support for large-scale, long-horizon research and realistic actions.These constraints limit conclusions about agents as research collaborators.Source: §1 · p. 2
  • InnovatorBench evaluates 20 open-ended LLM research tasks spanning data construction, filtering, augmentation, loss design, reward design, and scaffold construction.Tasks require agents to propose and refine methods, implement them, and submit concrete outputs; evaluation measures correctness, quality, and sometimes output uncertainty.Source: §1 · p. 2
  • ResearchGym supports realistic research workflows with broad actions, distributed long-running experiments, asynchronous monitoring, and snapshot saving and loading.It is extensible beyond InnovatorBench, allowing new tasks, datasets, and evaluation protocols.Source: §1 · p. 2
  • The experiments deploy a ReAct-based agent with several frontier LLMs to analyze performance across diverse LLM research subproblems.The reported potential includes handling code-based research tasks longer than 10 hours.Source: §1 · p. 2

2. Related Work

This section contrasts benchmarks focused on particular coding or reproduction skills with InnovatorBench’s broader LLM-research focus, and describes the environment capabilities ResearchGym adds for extended research workflows.

2. Related Work

  • Frontier LLMs show potential on code-based research tasks longer than 10 hours but struggle with fragile algorithm design and long-horizon decisions.Reported weaknesses include impatience, resource mismanagement, poor library choice, and reliance on template-based reasoning.Source: §2 · p. 3
  • ResearchGym is presented as a general, extensible environment for long-duration and distributed experiments, asynchronous execution, snapshots, and realistic research actions.The contribution also includes a broad action space and support for community-shared tasks and evaluation protocols.Source: §2 · p. 3
  • InnovatorBench evaluates end-to-end LLM research tasks across data, loss, reward, and scaffold design, while the related benchmarks discussed target narrower research aspects.The comparison table covers AI benchmarks across key evaluation dimensions; its Time Horizon is the time for the ReAct-based agent to reach its best score, which ML-Bench does not report.Source: §2 · p. 3, Table 1 · p. 3
  • Prior code-agent benchmarks cover repository issue resolution, scientific replication, experiment execution, reproducibility, and dataset discovery.The section distinguishes InnovatorBench by its broader focus on LLM-centric research abilities across the research lifecycle.Source: §2 · p. 3
  • Existing agent environments often constrain duration, scale, or action space and rarely support distributed training, asynchronous monitoring, and snapshots together.ResearchGym is presented as addressing these gaps while providing an extensible basis for new research tasks.Source: §2 · p. 3

3. InnovatorBench

InnovatorBench organizes research tasks derived from influential AI papers and their codebases into an end-to-end benchmark. Its task materials, evaluation procedure, and interaction loop support agents in exploring and submitting research implementations.

3. InnovatorBench

  • Each task links a research question to an open-source codebase and asks agents to explore methods and seek performance beyond the ground-truth solution.Source: §3 · p. 4
  • The benchmark comprises 20 tasks drawn from 14 influential papers across data construction, filtering, augmentation, loss design, reward design, and scaffold construction.Source: §3 · p. 4
  • Task descriptions specify motivation, objectives, data, constraints, evaluation, scripts, and environment details without prescribing step-by-step procedures.Source: §3 · p. 4
  • ResearchGym initializes a task workspace, supports iterative agent actions, and performs final evaluation before saving a state snapshot.Source: Figure 3 · p. 5
  • Tasks include a minimal runnable environment, reproducible datasets and checkpoints, and repositories adapted from original codebases with the key novelty removed.Agents may add scripts and files; provided datasets include training, validation, and test sets, with test labels removed.Source: §3 · p. 5
  • Hints are optional; the authors report that following one lets an experienced researcher gain about 80% of the score, while the main evaluation disables hint access.Source: §3 · p. 5
  • Evaluation accepts multiple submissions with immediate feedback, checks format validity, scores valid outputs against baseline and reference anchors, and runs externally to avoid reward hacking.Source: §3 · p. 5

4 ResearchGym

Prior agent systems run inside a single Docker container and execute commands synchronously. This design constrains experiment scale and reduces action throughput.

4 ResearchGym

  • Prior agent systems such as OpenHands and IterativeAgent operate in a single Docker container and execute commands synchronously, blocking the next action until the current one finishes.Source: §4 · p. 5
  • This synchronous, single-container design constrains experiment scale and reduces action throughput.Source: §4 · p. 5

5. Experiments and Results

Across six research-oriented tasks, the evaluated agents achieved nonzero scores, but performance varied by task type and models struggled with brittle algorithm design and long-horizon execution. InnovatorBench agents took substantially longer to reach peak performance than PaperBench agents, while ResearchGym supported distributed, asynchronous, long-running experiments.

5.1 Experimental Setup

  • ResearchGym provides 42 composable actions, multi-machine control, asynchronous command execution, and snapshots for pausing, resuming, or branching experiments.The evaluation used a ReAct-style agent with several frontier LLMs in a Docker environment, with additional cluster compute allocated by task.Source: §5 · p. 6, §5.1 · p. 6

5. Experiments and Results

  • ResearchGym separates action execution from selection so agents can continue planning while jobs run, then retrieve outputs asynchronously.Its pipeline dispatches commands to target machines, returns session status for asynchronous actions, and finalizes tasks with evaluation and a saved snapshot.Source: §5 · p. 6

5.2 Main Results and Findings

  • Claude Sonnet 4 achieved the highest average final and best scores and led four of six tasks; all three agents earned nonzero scores.The results also distinguish relatively robust data tasks from more brittle algorithm-related tasks.Source: §5.2 · p. 6, Table 2 · p. 7

5.3 Performance of model with Ground Truth Hint

  • Reliable tool use distinguished Claude Sonnet 4 on loss and reward design, while GPT-5’s high-frequency loop and GLM-4.5’s parameter errors disrupted training.GPT-5 also scored 60.07 on Scaffold Construction; its robust scaffolds used explicit options, up to three retries, and strict output formatting.Source: §5.3 · p. 7
  • Hints improved performance in Loss Design and Reward Design, but reduced it in Data Construction, Data Filtering, and Data Augmentation.In algorithm-related tasks, hints let agents replicate a ground-truth solution rather than explore; in data tasks, implementation errors and exact-replication failures could lower scores.Source: §5.3 · p. 7, Table 3 · p. 7, §5.4 · p. 8

5.4 Case Study

  • The case study identifies impatience, resource mismanagement, suboptimal library choice, and template-based reasoning as distinct agent failures.Examples include terminating a training run with substantial budget remaining, forgetting an active inference job, choosing Transformers over vLLM for high-throughput inference, and producing semantically vacuous CoT rationales.Source: Figure 4 · p. 8, §5.4 · p. 8
  • Small mismatches between a ground-truth hint and its reimplementation, especially in training or inference scripts, could leave performance below the no-hint symbolic method.The authors conclude that research performance depends on both creativity and code implementation ability.Source: §5.4 · p. 8

5.5 Test-time Scaling Performance

  • About 6.5× longer test time was needed to reach saturation on InnovatorBench than PaperBench, with peak performance reported after about 1.75 hours versus over 11 hours.The paper attributes the longer runtime to intricate tasks requiring extensive training, longer environment interactions, and iterative adjustments.Source: Figure 5 · p. 9, §5.5 · p. 9

6 Conclusion

The conclusion presents InnovatorBench and ResearchGym as complementary contributions for evaluating and supporting end-to-end LLM research agents. Experiments show promising code-based capabilities alongside weaknesses in algorithm design, resource management, and long-horizon planning.

6 Conclusion

  • InnovatorBench evaluates end-to-end LLM research across data construction, filtering, augmentation, loss design, reward design, and scaffold construction.ResearchGym supports large-scale, long-horizon experiments and realistic research workflows.Source: §6 · p. 9
  • Experiments with leading LLMs reveal promise in code-based tasks but weaknesses in reward design, resource management, and long-horizon planning.The conclusion frames the benchmark and platform as a foundation for real-world evaluation of AI agents.Source: §6 · p. 9

A Extended Details of the InnovatorBench Dataset

The appendix points readers to the benchmark’s task composition and task descriptions, and identifies a table comparing model performance across evaluation metrics.

A Extended Details of the InnovatorBench Dataset

  • Figure 6 shows InnovatorBench’s task composition, while Table 4 presents details of its tasks.The supplied table caption describes a performance comparison across models and evaluation metrics, without giving cell values.Source: A Extended Details of the… · p. 13, Table 4 · p. 13

B. Dataset Curation and Benchmark Construction Details SII-GAIR

The dataset-construction details describe both benchmark task examples and the process used to curate and score the 20 tasks. They include task-selection criteria, annotator effort, score calibration, and a difficulty adjustment to data augmentation.

B. Dataset Curation and Benchmark Construction Details SII-GAIR

  • Listed reinforcement-learning tasks include entropy-collapse prevention for mathematical reasoning, adaptive reward margins, search-augmented reasoning, and GUI grounding.The task descriptions name Verl as the framework for the reward-margin, search, and GUI tasks.Source: B. Dataset Curation and Benchmark… · p. 14
  • Other task descriptions cover deep research, multimodal mathematical and visual reasoning, dynamic theory-of-mind data, and scientific image-text augmentation.They specify systems or outputs such as source-attributed answers and structured JSON.Source: B. Dataset Curation and Benchmark… · p. 14

B Dataset Curation and Benchmark Construction Details

  • The 20 raw tasks were selected for reapplicability, potential improvement within two days, ability coverage, and use of common models.Thirteen annotators constructed workspaces and evaluation code, taking three days to two weeks per task; two authors then organized the tasks, workspaces, and evaluations into ResearchGym.Source: B Dataset Curation and Benchmark… · p. 14
  • Reference scores came from reapplying the original papers, while baseline scores often came from base models; score functions anchored baselines near zero and references around 80.The first benchmark version was adjusted by adding relevant scripts to the data-augmentation task after advanced models failed to generate and save SFT data correctly.Source: B Dataset Curation and Benchmark… · p. 14

C.1 Main Results

The main-results materials describe task-level model comparisons using scores and execution time, with the table caption also identifying cost. The supplied passages give comparison scope but no model outcome values.

C.1 Main Results

  • The results analysis compares models across tasks using final score, highest score, and runtime in hours.Table 5’s caption additionally identifies execution time and monetary cost in USD alongside final and best scores.Source: §C.1 · p. 15, Table 5 · p. 15

C.2 Performance of model with Ground Truth Hint

  • Table 6 compares Claude Sonnet 4 with and without a ground-truth hint on performance, execution time, and cost.The supplied passage does not report the comparison’s values.Source: §C.2 · p. 15

D Extended InnovatorBench Examples

  • The extended example describes entropy collapse as an overly deterministic output distribution early in reinforcement-learning training, limiting exploration of reasoning paths.It frames exploration of diverse strategies as important for long-form reasoning tasks.Source: D Extended InnovatorBench Examples · p. 15

D. Extended InnovatorBench Examples SII-GAIR

The SII-GAIR task asks agents to modify GRPO to improve accuracy while preventing entropy collapse, then train and evaluate the resulting policy under specified implementation and output constraints.

D. Extended InnovatorBench Examples SII-GAIR

  • Table 6 compares Claude4-hint and Claude4 on evaluation metrics, runtime, and cost.The supplied caption does not state the comparison’s results.Source: Table 6 · p. 16
  • The task is to implement a GRPO variant that seeks high accuracy while preventing entropy collapse.The evaluation checks both accuracy and entropy.Source: D. Extended InnovatorBench Examples… · pp. 16–17
  • Use the original GRPO rollout and modify only the advantage/loss computation in the specified Verl file.The provided background describes group responses and normalized group-level rewards.Source: D. Extended InnovatorBench Examples… · pp. 16–17
  • Train with the provided script, keep its parameters unchanged except test frequency, and select the best checkpoint for inference.The training script reports dev-set results and may be run multiple times.Source: D. Extended InnovatorBench Examples… · p. 17
  • Submit a Parquet file containing each test sample’s ID and complete response, plus logits for entropy calculation.The task requires the response to include reasoning and a boxed final answer.Source: D. Extended InnovatorBench Examples… · p. 17

E. Limitations and Future Works SII-GAIR

This passage set specifies the task’s resource and time limits, evaluation criteria, and constraints on modifying provided scripts.

E. Limitations and Future Works SII-GAIR

  • Training is limited to 24 hours and evaluation to 10 minutes, with eight 80GB H100 GPUs, three submission evaluations, and a 48-hour total limit.Source: E. Limitations and Future Works SII-GAIR · p. 18
  • Evaluation measures test-set accuracy and entropy during training; the stated score multiplies accuracy by entropy score and 100.The task also asks that MATH500 accuracy exceed the baseline and average entropy remain in a reasonable range.Source: E. Limitations and Future Works SII-GAIR · p. 18
  • Do not modify the provided scripts; the model-merger script converts Verl checkpoint weights into HuggingFace format.The conda environment is already set up, and a checkpoint in HuggingFace format is provided.Source: E. Limitations and Future Works SII-GAIR · p. 18

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