Evolving in Thought Space: Training a Small Model at Test Time Unlocks Better Discoveries
Open-ended scientific discovery often requires repeatedly proposing and evaluating candidate solutions. LLM-based systems can support this process by generating and refining executable solutions from verifier feedback. Methods such as TTT-Discover use test-time training (TTT) to update the solution-generating LLM from verifier feedback, adapting its generation policy to improve subsequent proposals on the target problem. However, this becomes expensive when reliable execution requires a large model, since training must maintain gradients, optimizer states, and policy statistics while repeatedly generating long, structured outputs. It also complicates credit assignment: outcome-level verif...
| Authors | Chonghe Jiang, Ao Qu, Siyuan Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 October 2026 |
| Last updated at source | 12 October 2026 |
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