Training AI Scientists to Replicate Research
Paper · 2026 · Damon Falck, Samer Sabri, Anja Surina et al.
Summary
Replicating published results is central to trustworthy science, but it is hard for AI agents. Papers leave out details, current models are mostly trained on well-defined problems, and replication offers no single metric to optimise. The authors built Replica, an automatically constructed task space. In each task, a results figure is hidden from a paper and the agent must regenerate it by actually running experiments under tight time and compute limits. Rewards come from a judge that is Codex-based, samples multiple times, and grades against a rubric generated for each task. The authors validated this judge against expert human rankings. With this reward, they post-trained Qwen3.6-27B into Faraday, an agent that delegates coding to Codex GPT-5.5. Training used a GRPO variant with turn-level credit assignment. Faraday outperformed Claude Opus 4.8 and GPT-5.5 on both in-distribution and held-out tasks. Qualitatively, it behaved more like a careful scientist, implementing real mechanisms rather than faking outputs. The work suggests that a small "research taste" layer can improve on much larger frontier agents in open-ended scientific tasks.
Key points
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Replica dataset: 310 figure-replication tasks drawn from 100 ML and AI-for-science papers published 1990–2026.
- The ML train split has 242 tasks; the held-out AI-for-science test split has 68.
- Figures were redacted with Gemini 2.5 Pro.
- Per-task rubrics were produced by Claude Opus 4.7 from a meta-rubric.
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Agent setup: each agent runs in a container with the redacted PDF, research libraries, internet access, and one-seventh of an H200 GPU (a MIG slice).
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Mean judge scores:
Model Train Test Qwen3.6-27B 0.678 0.554 Codex 0.796 0.729 Claude 0.828 0.748 Faraday 0.856 0.791 -
Head-to-head results: Faraday beats both frontier baselines on 73% of in-distribution tasks and 60% of held-out tasks.
- On the test split, this amounts to roughly 6% over Claude and 8% over Codex.
- Human experts confirmed the advantage.
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Scaled-up check: on 8 larger tasks (up to 8 B300 GPUs and 8 hours), Faraday beat Claude Opus 4.8 on 5 of the 8. Authors of four source papers also gave mixed but partly positive feedback on Faraday's replications of their work.
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksDecision Sciences · 306 worksInformation Systems and Management · 96 works
Cite
Cite as Damon Falck, Samer Sabri, Anja Surina, Thom Foster, Anya Sims, Sam Devlin, Dylan Rogers, Tantum Collins, Kaloyan Aleksiev, Louis Kirsch and Edward Hughes. 2026. Training AI Scientists to Replicate Research. arXiv:2608.13331.
@misc{falck2026training,
title = {{Training AI Scientists to Replicate Research}},
author = {Damon Falck, Samer Sabri, Anja Surina, Thom Foster, Anya Sims, Sam Devlin, Dylan Rogers, Tantum Collins, Kaloyan Aleksiev, Louis Kirsch and Edward Hughes},
year = {2026},
doi = {10.48550/arxiv.2608.13331},
eprint = {2608.13331},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2608.13331}
}References
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