Hugging Face Journal Club: Training AI Scientists to Replicate Research
The discussion summarizes research on Faraday-27B, a model trained by Inherent designed for scientific replication—the ability to reproduce results from redacted ML/AI papers. The system uses Reinforcement Learning (RL) and integrates CodeX as a tool, allowing the agent to execute code within a simulated environment. Key methodological advances include using sophisticated rubric-based judges (generated via Claude) instead of simple verifiers, employing multi-rollout averaging to mitigate variance, and implementing weighted credit assignment across the agent's steps.
Key takeaways
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Scientific Replication Task
The model is tasked with replicating missing figures from redacted ML/AI papers. This process requires the agent to use tools (like CodeX) and execute code in a simulated environment, moving toward full automation of AI R&D.
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Advanced Judging Mechanism
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Instead of simple verification, the system uses a rubric-based judge (generated by Claude) that assigns fine-grained points for correct reasoning, figure accuracy, and code writing. This process involves averaging judgments across multiple rollouts to prevent reward hacking.
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Performance & Scaling
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The trained Faraday model demonstrated strong performance, sometimes outperforming much larger models like Claude and GPT-5. Furthermore, the system showed generalization even when given increased compute resources (e.g., scaling up to 8 hours/8 B300s).