Training autoresearch agents with RL by separating what to investigate from how
anirudhg9119 · x · 2026-10-14
A new paper tackles how to train autoresearch agents: when agents run hundreds of experiments, how do we assign credit to decisions whose payoff arrives much later?
Key ideas:
- A harness separates ideation from execution, so credit can be assigned directly to meta-reasoning decisions
- A decision-level generative critic, pre-trained across many environments, serves as a transferable value prior for partial progress
- The same model is co-trained as both actor and critic, learning to value partial states and act from them
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