EvoLib enables LLMs to accumulate, reuse, and evolve knowledge abstractions from inference trajectories at test time, yielding substantial gains on math reasoning, code generation, and agentic benchmarks without parameter updates or supervision.
Autorefine: From trajectories to reusable expertise for continual llm agent refinement
8 Pith papers cite this work. Polarity classification is still indexing.
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This survey introduces the Generate-Filter-Control-Replay (GFCR) taxonomy to structure rollout pipelines for RL-based post-training of reasoning LLMs.
A systematic study across five domains finds model-generated skills yield average gains but non-uniform negative transfer, with a meta-skill improving extraction quality.
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From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills
A systematic study across five domains finds model-generated skills yield average gains but non-uniform negative transfer, with a meta-skill improving extraction quality.
- SkillOpt: Executive Strategy for Self-Evolving Agent Skills
- Evidence Over Plans: Online Trajectory Verification for Skill Distillation
- From Context to Skills: Can Language Models Learn from Context Skillfully?