Training AI Scientists to Replicate Research

Key points

Faraday, a 27B small model, uses a 5-trillion-parameter coding agent as a tool to outperform Claude and GPT-class systems on paper replication, which suggests that it is better to instill scientific judgment in a smaller model than to use a large model directly.
For open-ended tasks that cannot be fully verified, it presents a recipe in which an automatic rubric judge aligned with human evaluation is used as the reward for stable reinforcement learning.
It achieves a 60 percent win rate and an average gain of 6 percent on held-out AI-for-science tasks, which shows that it transfers scientific reasoning rather than merely memorizing.
Faraday does not produce results by blindly coding outputs. Instead, it directly implements the paper's mechanism and scales the experiments down to fit the budget, much like a human scientist.
The fact that a weaker model can supervise and direct a stronger model becomes practical evidence relevant to AI safety and oversight research.

External research summaries. These are not HDATF publications or measured product results.