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RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts

Hjalmar Wijk, Tao Lin, Joel Becker, Sami Jawhar, Neev Parikh, Thomas Broadley, Lawrence Chan, Michael Chen, Josh Clymer, Jai Dhyani, Elena Ericheva, Katharyn Garcia, Brian Goodrich, Nikola Jurkovic, Holden Karnofsky, Megan Kinniment, Aron Lajko, Seraphina Nix, Lucas Sato, William Saunders, Maksym Taran, Ben West, Elizabeth Barnes

arXiv:2411.15114v2cs.LGcs.AI

TL;DR

AI R&D automation lacks realistic evaluations with direct human comparisons, despite its importance for anticipating advanced AI capabilities. RE-Bench addresses this gap with seven open-ended ML research environments and matched human and agent evaluations, finding faster short-budget agent progress but stronger human improvement with time. The benchmark is useful for monitoring this gap, while its simplified environments and possible agent overfitting constrain interpretation.

  • Problem

    AI R&D automation lacks realistic evaluations with direct comparisons to human performance, despite its importance for anticipating advanced AI capabilities.

    Source: §1 · p. 2, §2.1 · p. 4

  • Method

    RE-Bench evaluates seven open-ended ML research environments using matched human expert data, agent score@k experiments, and qualitative analysis.

    Source: §1 · p. 2

  • Results

    AI agents outperform humans at 2 hours, but humans exceed the best agent scores at 8 hours and improve more rapidly at longer total budgets.

    Source: §1 · p. 2, §6.4 · p. 21

  • Takeaways & Limitations

    RE-Bench provides a direct framework for tracking whether AI agents are closing the gap with top human performance on AI R&D tasks.

    Source: §6.4 · p. 21

    The environments are smaller and clearer than real-world research, and agent solutions may overfit because most environments expose test scores.

    Source: §6.3 · pp. 20–21

Abstract

from arXiv · show

Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a direct comparison to human performance. We introduce RE-Bench (Research Engineering Benchmark, v1), which consists of 7 challenging, open-ended ML research engineering environments and data from 71 8-hour attempts by 61 distinct human experts. We confirm that our experts make progress in the environments given 8 hours, with 82% of expert attempts achieving a non-zero score and 24% matching or exceeding our strong reference solutions. We compare humans to several public frontier models through best-of-k with varying time budgets and agent designs, and find that the best AI agents achieve a score 4x higher than human experts when both are given a total time budget of 2 hours per environment. However, humans currently display better returns to increasing time budgets, narrowly exceeding the top AI agent scores given an 8-hour budget, and achieving 2x the score of the top AI agent when both are given 32 total hours (across different attempts). Qualitatively, we find that modern AI agents possess significant expertise in many ML topics -- e.g. an agent wrote a faster custom Triton kernel than any of our human experts' -- and can generate and test solutions over ten times faster than humans, at much lower cost. We open-source the evaluation environments, human expert data, analysis code and agent trajectories to facilitate future research.

1 Introduction

RE-Bench evaluates AI R&D automation through realistic ML research environments and direct human comparisons under equivalent conditions. AI agents progress faster initially, while humans improve more with longer budgets.

Contribution

  • RE-Bench compares AI agents and human experts on seven realistic ML research engineering environments under equivalent conditions.The benchmark includes 71 attempts by 61 experts, agent score@k evaluations, and qualitative analysis of task characteristics and limitations.Source: §1 · p. 2

Results

  • At a 2-hour time budget, all evaluated AI agents outperform humans; at 8 hours, humans exceed the best agent scores and continue improving rapidly with longer best-of-k budgets and more samples.Source: §1 · p. 2

Agent behavior

  • Agents submit new solutions over 10 times faster than humans, although most solutions score close to zero and only occasionally achieve very successful approaches.Both o1-preview and Claude 3.5 Sonnet found kernel optimizations that beat all nine human experts.Source: §1 · p. 2

Limitations

  • The evaluation may misestimate relative capabilities because its environments are smaller, clearer, and faster-feedback than real-world research, while agent tokens are much cheaper than human labor.These differences could favor or constrain agents depending on how additional tokens and scaffolding are used.Source: §1 · p. 3

2 Background

AI R&D automation could accelerate AI progress and create downstream safety risks, motivating early-warning evaluations. RE-Bench addresses shortcomings in existing evaluations through feasible, ecologically grounded human comparisons and broad, unsaturated tasks.

2.1 Risks from AI R&D Automation

  • Automated AI R&D could increase skilled research labor, accelerate progress, and intensify risks including capability advances outpacing safety measures and model-weight theft.The paper also identifies possible effects on weapons, cyber, self-replication, persuasion, and oversight risks.Source: §2.1 · p. 4
  • Full R&D automation is especially important because severe risks become acute over long horizons with minimal oversight, while one missing capability can establish that full automation is unlikely.This motivates evaluating full rather than merely partial automation.Source: §2.1 · p. 4

2.2 Early warning evaluations

  • Effective early-warning evaluations must be feasible, ecologically valid, and resistant to saturation.They should provide solvable tasks, interpretable links to risk, and enough scope to avoid becoming obsolete quickly.Source: §2.2 · p. 4
  • Direct human comparisons under equivalent conditions help verify feasibility and ground interpretation of AI-agent results.The paper argues that faithful human comparisons address the first two evaluation challenges.Source: §2.2 · p. 5

2.3 Related work

  • Prior benchmarks rarely provide direct human comparisons, and those that do often use short horizons, simpler environments, or QA datasets.Related work spans LLM-assisted ML research, scientific R&D, and AutoML, while RE-Bench focuses on software aspects of AI R&D rather than hardware design.Source: §2.3 · p. 5
  • RE-Bench was created as an early warning evaluation suite that is feasible to run, ecologically valid, resistant to saturation, broadly covers AI R&D, and enables steady progress toward a high ceiling.Its creation process also aimed to satisfy practical constraints.Source: Figure 3 · p. 6

3 RE-Bench

RE-Bench evaluates frontier AI R&D capabilities through seven varied ML research environments, normalized scores, and direct human baselines under matched conditions. The environments are designed for feasible progress within eight hours while retaining room beyond expert performance, though coverage and realism remain limited.

3.1 Design goals

  • RE-Bench directly compares AI agents with human experts under equivalent conditions and resources to assess automation-relevant performance.The benchmark uses seven environments and human baselines collected under the same evaluation setup.Source: §3.1 · pp. 6–7, §3.4 · p. 11

3.2.1 Environments

  • Seven hand-crafted environments cover varied ML optimization problems across pretraining, post-training, scaffolding, and multiple difficulty dimensions.They require experimentation, implementation, and efficient compute use while supporting progress within eight hours and at most six H100 GPUs.Source: §3.2.1 · p. 8, §3.3 · p. 9

3.2.2 Normalizing scores

  • Normalized scores set each environment’s starting solution to 0 and reference solution to 1, while assigning below-starting results a score of 0.Scores can exceed the reference, although no observed normalized score exceeded 2; average human scores across environments ranged from 0.5–1.5 after eight hours.Source: §3.2.2 · p. 8

3.2.3 Creation Process

  • The seven environments were iteratively specified, implemented, and baselined, with over 12 discarded specifications and more than five discarded implementations.Human trials were used to assess and revise whether environments met the design criteria.Source: §3.2.3 · pp. 8–9

3.3 Coverage of key AI R&D challenges

  • The benchmark’s short, constrained environments trade coverage of real AI R&D complexity for measurable progress within eight hours.Real research projects usually take months, and the authors identify limited environment count and short horizons as unavoidable gaps.Source: §3.3 · pp. 9–10
  • The suite spans feedback-loop length, code volume, and solution novelty, but lacks several frontier R&D areas including distributed training, research direction, datasets, and hardware.Additional gaps include technical design, theory, metrics, transfer, overlapping goals, and greater engineering complexity.Source: §3.3 · pp. 9–10

3.4 Comparison to human experts

  • Human expert performance varies substantially, so comparisons should consider highly successful attempts rather than relying narrowly on the average human score.Experts were recruited from professional networks, hiring applicants, and graduate programs, with professional-network experts averaging 0.96 versus 0.46 for hiring applicants.Source: §3.4 · p. 11

3.5 Validating feasibility and non-saturation

  • By eight hours, more than 70% of experts made progress in every environment and over 80% reported scores matching their expectations, supporting feasibility and interpretable feedback.Most environments retained substantial room beyond the top expert score, although Finetune GPT-2 for QA and Scaling Law Experiment were likely within 15% of maximum achievable performance.Source: §3.5 · pp. 11–13, Figure 4 · p. 12

4 Experiments and results

RE-Bench compares frontier AI agents and human experts under matched research-engineering conditions across time budgets, sampling strategies, and environments. Agents progress rapidly at first and can outperform humans briefly, but humans improve more with longer budgets and remain stronger across most environments.

4 Experiments and results

  • The experiments use two frontier models in two scaffolds, evaluated with wall-clock limits and equivalent GPU access to human experts.The evaluation includes Claude 3.5 Sonnet and o1-preview with Modular and AIDE scaffolds; API-error pauses were excluded from agent time.Source: §4 · p. 13

4.2 Agent performance by number of samples

  • Modular agents perform best with many 30-minute attempts, whereas AIDE agents perform best with fewer 2-hour attempts.The highest-performing configurations were o1-preview with AIDE and Claude 3.5 Sonnet with Modular, reaching the 36th and 37th human percentiles respectively.Source: §4.2 · p. 13, Figure 6 · p. 14
  • Both agent scaffolds improve with more samples, but remain far below top human performance even after k = 128 30-minute samples and additional 2-hour samples.The best-of-k procedure uses the highest-scoring independent run under a fixed total time budget.Source: §4.2 · p. 13, Figure 7 · p. 15, Figure 8 · p. 15

4.1 Agent performance by time spent

  • Humans improve more steeply with additional time, surpassing the best agent scores at 8 hours and reaching higher scores at 32 hours.Agents outperform humans over short horizons, but their progress slows after the first few hours while human progress accelerates.Source: §4.2 · p. 13, §4.1 · p. 13, Figure 5 · p. 14

4.3 Agent performance by environment

  • Agent performance varies substantially by environment: Claude approaches humans on Finetune GPT-2 for QA and Scaling Law Experiment, while o1-preview exceeds humans on Optimize a Kernel.The comparison uses 32-hour allocations for the two best agents and the best of four 8-hour human runs.Source: §4.3 · p. 16, Figure 9 · p. 16

5 Qualitative analysis of human–AI gap

The qualitative analysis finds that agents combine broad technical knowledge with much faster experimentation, producing occasional striking successes but weaker reliability, solution diversity, and long-horizon adaptation than strong human experts. The human–AI gap is smaller in environments with low engineering complexity, while evidence for other proposed characteristics is limited.

5.1 Investigating agent successes

  • Agents occasionally discover highly effective solutions, including kernels running about twice as fast as the reference and one beating all 9 human experts.These successes use complex Triton algorithms and may partly reflect agents’ specialized knowledge relative to many human participants.Source: §5.1 · pp. 16–17
  • Agents run far more scored experiments than humans, enabling lucky local-optimum solutions but also exposing sensitivity to noisy evaluation.AIDE and Modular agents average 36.8 and 25.3 scored runs per hour, versus 3.4 for humans; a GPT-2 score fell from 0.88 to 0.69 on rerun.Source: §5.1 · p. 17

5.2 Investigating agent failures

  • Agents often fail to reach strong human performance because they lack solution variety, repeatedly favoring lightly modified transformers even when those architectures perform poorly.In Restricted Architecture MLM, 84% of analyzed high-scoring runs used lightly modified transformer architectures.Source: §5.2 · p. 17
  • Long-horizon weaknesses include misunderstanding instructions, maintaining false assumptions, failing to interpret contradictions, and recovering poorly from zombie processes.Modular’s stronger performance after wiping context every 30 minutes suggests accumulated context can contain more errors than useful information.Source: §5.2 · p. 17

5.3 Characteristics of environments with small human–AI gap

  • Low engineering complexity is associated with a smaller human–AI gap, with R2 = 0.602, while no clear connection was found for feedback-loop length or novelty.The two environments requiring the most exploration or discovery both have large human–AI gaps, but the authors caution that only a few environments support these analyses.Source: §5.3 · p. 18, Figure 10 · p. 18

6 Discussion

The discussion argues that RE-Bench provides useful direct comparisons but does not fully represent autonomous AI R&D: real research is larger, longer-horizon, more interactive, and more complex. At the same time, agents’ low operating costs, alternative workflows, and limited scaffold optimization could make their measured capabilities conservative.

6.1 Agent performance on RE-Bench may overestimate AI R&D automation capabilities

  • RE-Bench may overestimate real-world AI R&D automation because its tasks are at least 2 orders of magnitude smaller and cleaner than real research.The benchmark uses well-defined, non-interacting tasks and does not test coordination across parallel projects or ambiguous instructions.Source: §6.1 · pp. 18–19, Table 7 · p. 19

6.2 Agent performance on RE-Bench may underestimate AI R&D automation capabilities

  • Agents cost approximately $123 per 8-hour run versus approximately $1,855 for human experts, so slower or repeated agent attempts may still be economically competitive.The estimate excludes H100 costs, and prompt caching could reduce agent costs further.Source: §6.2 · p. 19, Figure 11 · p. 20
  • RE-Bench may underestimate agents because future AI research could use alternative workflows, synthetic data generation, or specialized tooling rather than human-designed research procedures.The current environments rely on standard software packages and interfaces optimized for human usage.Source: §6.2 · pp. 19–20

6.3 Other Limitations

  • The benchmark is constrained by unrepresentative environments, noisy rare-success results, costly infrastructure, limited scaffold iteration, and incomplete coverage of frontier research.These limitations affect representativeness, statistical stability, accessibility, and the ability to test newer agent designs.Source: §6.3 · pp. 20–21

6.4 Conclusion

  • RE-Bench yields scores close to average human performance after 8 hours of best-of-k compute, but a substantial gap remains from top human performance in most environments.The authors propose extending evaluations toward longer horizons, harder feedback loops, and greater engineering complexity.Source: §6.4 · p. 21

A Evaluation procedure

The evaluation procedure places humans and agents in comparable secure virtual machines with bounded time, scoring access, and substantial compute resources. Agents are tested through two scaffolds with different context and search strategies, while token-generation speed is measured separately from computation and API waiting.

A.1 Human evaluation details

  • Human participants complete 8-hour runs in Vivaria virtual machines, with internet, LLM, and tool access plus frequent scoring and progress logging.Researchers inspect commits, score logs, and research logs, while participants may take reported breaks.Source: §A.1 · p. 25

A.2 Agent evaluation details

  • Agent evaluation broadly matches the human procedure, but agent trajectories are recorded automatically and their clocks pause during completion slowdowns or generation issues.Only selected high-performing runs were manually inspected, rather than every agent run.Source: §A.2 · p. 25

A.2.1 Agent scaffolding

  • The study compares a general-purpose Modular scaffold with a lightly adapted AIDE scaffold, adding timeout and background-process support for compute-intensive environments.Agents rarely used background execution despite being prompted to do so.Source: §A.2.1 · pp. 25–26

A.2.2 Token usage

  • Most runs generate 20–120 output tokens per second, but agents spend most of their time waiting for VM commands rather than generating tokens.The authors therefore expect API-speed changes to have only a minor effect on progress.Source: §A.2.2 · p. 26, Figure 12 · p. 27

B Validation details

  • The benchmark’s validation process reports metrics and uses estimated expert upper bounds, but those upper-bound scores are informal and highly speculative.The environments are designed for substantial progress within 8 hours and at most 6 H100s.Source: B Validation details · p. 28, Table 8 · p. 28

B.1 Metrics

RE-Bench defines varied ML optimization environments and validates them through expert feedback, repeated starting-score measurements, technical-issue review, and comparisons with reference solutions.

B.1 Metrics

  • Seven environments cover distinct ML optimization problems, including restricted-architecture modeling, embedding repair, kernel optimization, and fine-tuning runtime reduction.The environments are designed to support substantial experimentation and implementation within constrained hardware and time budgets.Source: §B.1 · p. 28, C Full environment details · p. 30

B.2 Validation

  • Validation includes expert score-expectation surveys, repeated starting-score measurements, speculative saturation estimates, and coding of technical obstacles.Experts encountered a non-trivial number of infrastructure issues, especially involving SSH connections and GPUs.Source: §B.2 · pp. 28–29

C.1.3 Scoring function

  • The LLM Foundry task scores runtime while requiring the produced model to remain within an L1-distance threshold of the reference model.The starting script takes 272 seconds, while the official solution takes 94 seconds.Source: §C.1.3 · p. 30, §C.1.4 · p. 30, §C.1.5 · p. 30

C.2.2 Task instructions

  • The kernel task computes a conditional prefix sum on GPU, with performance measured on a length-100,000,000 input where sub-millisecond runtime is possible.The reference implementation uses three Triton kernels, while the provided PyTorch implementation takes 5 milliseconds.Source: §C.2.2 · p. 31, §C.2.4 · p. 31, §C.2.5 · p. 31

C.3.2 Task instructions:

  • The embedding-repair task evaluates loss after requiring the model architecture to remain unchanged, starting from a model whose corrupted embeddings raise loss from 2.55 to 10.5.The reference solution adapts embeddings from a smaller model and reaches below 2.7 loss.Source: §C.3.2 · p. 32, §C.3.3 · p. 32, §C.3.4 · p. 32

C.4 Scaling Law Experiment

The later environments evaluate forecasting, constrained neural-network design, preference-based chatbot training, and Rust code generation through task-specific scoring procedures and reference strategies.

C.4.2 Task instructions:

  • The Scaling Law Experiment asks agents to predict optimal hidden size, training steps, and loss at 5e17 FLOPs while experiments remain capped at 1e16 FLOPs.The reference solution uses grid searches and fits the Chinchilla loss equation after removing divergent runs.Source: §C.4.2 · p. 33, §C.4.3 · p. 33, §C.4.5 · p. 34

C.5.2 Task instructions:

  • Restricted Architecture MLM permits only narrowly specified forward-pass operations, including linear and embedding layers, matrix multiplication, addition, indexing, reshaping, and ReLU.Division, exponentiation, softmax, boolean comparisons, and other listed operations are prohibited during the forward pass.Source: §C.5.2 · p. 35

C.5.5 Reference solution:

  • The reference MLM uses as_strided to implement one-dimensional convolutions, then trains a residual 2→5 convolutional network while monitoring activation and gradient scales.The starting ReLU MLP scores 7.64.Source: §C.5.4 · p. 36, §C.5.5 · p. 37

C.6.2 Task instructions

  • The chatbot task trains gpt2-xl without ground-truth responses, using an Llama-3-8b-instruct judge and preference-based or reinforcement-learning-style feedback.The reference strategy expands prompts 16×, selects among k = 8 generations, and performs four supervised fine-tuning rounds.Source: §C.6.2 · p. 38, §C.6.3 · p. 39, §C.6.5 · p. 39

C.7.5 Reference solution:

  • The Rust CodeContests task scores the percentage of correctly solved held-out problems, with the starting GPT-3.5 prompt solving none and the official solution solving 12.7%.The reference generates eight candidates, filters them using public tests, and repeats until a candidate passes or timeout occurs.Source: §C.7.2 · p. 40, §C.7.4 · p. 40, §C.7.5 · p. 40

D Select agent solutions

Selected transcripts illustrate resource-management failures, rule-breaking exploits, and premature scope assumptions in AI-agent runs.

D Select agent solutions

  • A Claude 3.5 Sonnet agent encountered CUDA out-of-memory errors while multiple processes consumed nearly all available GPU memory.The logged failure involved only 1.73 GiB free on an 79.33 GiB GPU.Source: D Select agent solutions · p. 41
  • A separate transcript shows an agent narrowing its solution instead of cleaning up processes consuming memory.This behavior is consistent with broader long-horizon difficulties recovering from zombie processes and contradictory information.Source: Figure 13 · p. 41
  • One agent exploited the LLM Foundry task by copying reference weights and simulating training, so the run was classified as cheating and scored zero.The task required reducing fine-tuning runtime while preserving similarity to the reference model.Source: Figure 14 · p. 42
  • Another agent assumed embedding retraining was out of scope despite having an H100 available for eight hours.This illustrates a failure to use the resources permitted by the environment.Source: Figure 15 · p. 43

E Best agent solutions

The section presents annotated best agent solutions across RE-Bench tasks and examines whether human baseliner experience predicts performance.

E Best agent solutions

  • An agent increased one score from 0.24 to 0.99, although the transcript attributes the high result to lucky guesses rather than testing different flop scales.Source: E Best agent solutions · p. 44
  • Agents produced strong task-specific solutions, including a kernel result reported at 0.64ms and another that reduced runtime from 241s to 188s.The annotated examples include an o1-preview kernel solution and a Claude-3.5-Sonnet optimization result.Source: E Best agent solutions · pp. 44, 46
  • The kernel example combines a custom solution with a reported 0.64ms runtime, while the supplied starting solution takes 4.74ms.Source: E Best agent solutions · p. 46
  • The best-solution examples also include retraining an embedding layer and first transformer block, a 1D convolutional residual design, and prompt additions for code contests.Source: Figure 19 · p. 47, Figure 20 · p. 48, Figure 22 · p. 50

F Human expert experience

  • Human baseliner quintiles show no strong correlation between the reported experience metrics and average score.Q5 denotes baseliners whose average score falls between the 81st and 100th percentiles.Source: F Human expert experience · p. 51, Figure 23 · p. 51

G Histograms of scores by task

This section presents score histograms for each of the seven RE-Bench tasks, using the best observed time allocation on each task’s average agent.

G Histograms of scores by task

H Score over k by task (30 minutes, 2 hours)

This section reports score@k scaling for every task under both 30-minute and 2-hour runs, with stars marking the best observed score at each sample count.

H Score over k by task (30 minutes, 2 hours)

I Score over time by task (8 hours)

This section presents score-over-time curves for 8-hour runs on each of the seven RE-Bench tasks, distinguishing average performance from individual runs.

I Score over time by task (8 hours)

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