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This paper formalizes SFIA-based skill and responsibility-level extraction from free text, evaluating five strategies (lexical baseline, dense retrieval with LLM reranking, zero-shot schema-constrained LLM, single-agent agentic RAG, and a three-agent retriever-matcher-Verifier crew), finding that explicit level prediction is key and multi-agent crews add latency without accuracy gains.
SKILLER is a natural-language-driven reinforcement learning framework that automatically generates executor-specific agent skills for small language models, matching closed-source performance at lower cost.
This paper proposes SEED, a method that internalizes post-hoc skills from trajectories into model parameters through self-evolving online distillation, solving the reward sparsity problem in long-horizon RL training, achieving significant improvements on benchmarks such as ALFWorld.
Codex introduces a new feature that automatically analyzes user historical memory with a single prompt, extracts repetitive workflows, and converts them into evolvable skills, automating daily repetitive tasks.
This paper systematically evaluates model-generated skills for language agents across the full lifecycle of experience generation, extraction, and consumption, finding that skills are beneficial on average but exhibit non-trivial negative transfer, leading to a meta-skill that improves skill quality.