SelfSearch: Reward-Free Search for Self-Improving Agents
Advances in the coding capabilities of LLM agents allow them to inspect and modify their own instructions, tools, and execution procedures. Existing approaches use this ability to search for improved agents through repeated downstream evaluation, which incurs substantial costs and ties the search to the evaluated tasks. We introduce \textbf{SelfSearch}, a reward-free search procedure in which agents modify themselves using records of previous self-improvement episodes. These records capture the reasoning, tool actions, and outcomes of earlier modification attempts, providing concrete experience for improving both task solving and self-modification. Without downstream reward signals during...
| Authors | Jungwoo Yang, Injin Kong, Yohan Jo |
|---|---|
| Primary category | cs.AI |
| Published | 29 September 2026 |
| Last updated at source | 30 September 2026 |
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