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Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment across long interaction sequences. Recent work on on-policy self-distillation (OPSD) offers complementary supervision by evaluating a policy's...
These results support accounting for selection history when constructing and evaluating search-agent rollouts and rank first on seven QA benchmarks using Qwen3-4B, Qwen3-8B, and Qwen2.5-7B.
The results show that evolving skill memory is not merely an inference-time plug-in: it changes policy learning and the future training distribution while retaining value as optional external memory.
CoEvoKG is introduced, a framework that turns a knowledge graph into both a source of verifiable training tasks and a persistent evidence memory for agent evolution, closing the loop between model self evolution and knowledge accumulation.
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