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#SkillGym

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Qwen3.5 4微调 4智能体 4Claude Code 2Terminal-Bench 1开源模型 1
Stories, products and papers tagged "SkillGym", newest first.
Oct 6
PapersBuildersMulti-sourceSelected06:46
Shanghai AI Laboratory SkillGym trains better agents from verified skill runs

Training on verified execution traces can bake retrieval-dependent prompt skills into model weights, reducing reliance on the agent finding and following instructions at runtime. Teams building coding agents can replicate the recipe by turning their own skill libraries into checkable sandbox tasks. The work comes from a Chinese lab and benchmarks on Claude Code with Qwen, making it a useful reference point against prompt-based skill systems like Claude Skills.

#Shanghai AI Laboratory#Qwen#SkillGym#agents
Event
rohanpaul_ai@rohanpaul_ai3 sourcesSource
Sep 28
PapersBuildersMulti-sourceSelected73°06:39
SkillGym trains Qwen3.5-35B-A3B on 2,756 skill environments

Continual training on distilled skills outperforming a larger-context approach suggests skill libraries can be baked into weights rather than retrieved at inference time. Teams building terminal or coding agents can test whether the same recipe transfers to their own models and checkers. The result also matters for anyone benchmarking open-weight Qwen models against Claude Sonnet 4.6 and GPT-5.4 Mini on agentic tasks.

#Qwen#SkillGym#fine-tuning#agents
Event
DAIR.AI@dair_ai3 sourcesSource
Sep 25
PapersBuildersMulti-sourceSelected12:11
SkillGym turns human agent skills into RL training environments

Training on internalized skill workflows lets a 35B open-weights model beat far larger proprietary models on agentic benchmarks, shifting the edge from raw model scale to curated environment data. Builders running Claude Code or Codex harnesses can reuse the released environments and trajectories for their own SFT or RL pipelines. The results give Qwen-based deployments a concrete benchmark point against Claude Sonnet 4.6 and DeepSeek V4 Pro.

#SkillGym#Qwen3.5#agents#reinforcement learning
Event
arXiv: DeepSeek@Zhilong Ge et al. (9)6 sourcesSource
Sep 24
PapersBuildersMulti-sourceSelected09:35
SkillGym Turns Human Agent Skills into Training Environments

The key finding is that skills can be internalized as model weights rather than injected at inference time, since the fine-tuned agent beats skill-assisted base models even without the skills provided. Agent engineers and fine-tuning practitioners can reuse the released environments and trajectories for SFT or RL with outcome-based rewards. The result also shows a 35B open Qwen-based model outscoring much larger closed models on SkillsBench, a useful cost comparison for teams building coding and tool-use agents.

#SkillGym#Qwen3.5#agents#fine-tuning
Event
arXiv: DeepSeek@Zhilong Ge et al. (9)6 sourcesSource