This survey introduces the Generate-Filter-Control-Replay (GFCR) taxonomy to structure rollout pipelines for RL-based post-training of reasoning LLMs.
Autorefine: From trajectories to reusable expertise for continual llm agent refinement
9 Pith papers cite this work. Polarity classification is still indexing.
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HExA is a training-free agent framework that improves LLM performance on novel physics tasks from 2% to 77% by iteratively designing experiments and composing learned skills.
SkillOpt introduces a controllable text-space optimizer that evolves agent skills via add/delete/replace edits accepted only on strict held-out validation improvement, reporting consistent gains across 52 model-benchmark-harness combinations.
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.
EvoLib improves black-box LLM test-time performance by maintaining an evolving, self-scored library of reusable skills and insights, without parameter updates or ground-truth feedback.
SPARK generates environment-verified trajectories to compute PDI, enabling posterior skill distillation that outperforms no-skill baselines and human-written skills across 86 tasks with up to 1000x cheaper inference.
Ctx2Skill automatically produces natural-language skill files via self-play between a challenger, a reasoner, and a judge, improving LLM performance on context-learning tasks.
Compact control-oriented strategy genes outperform documentation-heavy skill packages for test-time guidance and iterative experience evolution on scientific coding tasks.
A survey that defines agent skills as reusable procedural artifacts and reviews methods, resources, and applications across their representation, acquisition, retrieval, and evolution stages.
citing papers explorer
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Hierarchical Experimentalist Agents
HExA is a training-free agent framework that improves LLM performance on novel physics tasks from 2% to 77% by iteratively designing experiments and composing learned skills.
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SkillOpt: Executive Strategy for Self-Evolving Agent Skills
SkillOpt introduces a controllable text-space optimizer that evolves agent skills via add/delete/replace edits accepted only on strict held-out validation improvement, reporting consistent gains across 52 model-benchmark-harness combinations.
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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.
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Evidence Over Plans: Online Trajectory Verification for Skill Distillation
SPARK generates environment-verified trajectories to compute PDI, enabling posterior skill distillation that outperforms no-skill baselines and human-written skills across 86 tasks with up to 1000x cheaper inference.
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From Context to Skills: Can Language Models Learn from Context Skillfully?
Ctx2Skill automatically produces natural-language skill files via self-play between a challenger, a reasoner, and a judge, improving LLM performance on context-learning tasks.