Hugging Face Trending Papers
SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
Hugging Face Trending Papers
Aug 11
SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models
Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution. However, because strong closed-source models entail high inference costs, current popular agent harnesses, such as Codex and OpenClaw, remain prohibitively expensive when deploying these skills to accomplish real-world tasks.
arXiv AI
Aug 18
SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion
arXiv:2608. 15165v1 Announce Type: new Abstract: Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge.
arXiv AI
Aug 12
SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models
arXiv:2608. 10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution.
arXiv AI
1d ago
SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation
SkillGym is an automatic pipeline that generates verifiable environments for training skill-use agents. It crawls internet skills, filters for reproducible workflows, and uses a builder‑reviewer process to create difficulty‑controlled tasks with reference solutions and verifiers. The system builds 6.8k environments, collects 19k successful trajectories, and fine‑tunes LLMs from 2B to 122B parameters, improving performance and skill invocation rates.
arXiv Machine Learning
Jul 30
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.
arXiv AI
Aug 11
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
arXiv:2604. 08377v2 Announce Type: replace Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment.