Skill selection, not RL, drives the gain — 8.3 of 10.4 points

Notes on OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models (arXiv:2606.16774) — Tianyi Lin, Chuan-da Sun, Jingyi Zhang, Changxu Wei, Huanjin Yao, Shunyu Liu, Xikun Zhang, Liu Liu, Jiaxing Huang · June 2026

Note published July 24, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

OpenClaw-Skill builds a searchable tree of skills instead of a flat library. Its construction method, Collective Skill Tree Search (CSTS), breaks a long-horizon agentic task into an ordered chain of subtasks. For each one, several heterogeneous LLMs attempt it independently, and each resulting trajectory gets distilled into a candidate skill node. Every candidate is scored twice. A collective quality score averages judgments across multiple judge models; a transferability score hands the skill to models that never produced it and checks how well those outsiders perform using it as guidance. Only the top-scoring node per subtask survives into the tree, and the surviving path becomes supervised training data. Collective Skill Reinforcement Learning (CSRL) then layers a GRPO-style policy update on top: rollouts conditioned on different candidate skills for the same subtask are normalized against each other as one group, so the policy learns to prefer whichever skill actually worked, not whichever one it happened to sample.

Across four Qwen backbones (Qwen3-4B/8B and Qwen3.5-4B/9B), OpenClaw-Skill lifts overall QwenClawBench scores by 4.3 to 10.4 points. The flagship Qwen3.5-9B variant moves from 34.5 to 44.9. Gains concentrate in categories tied to verification and error recovery: the 9B model's CS category jumps from 30.2 to 78.4, and its SVM category from 33.2 to 70.9. On PinchBench's 123-task suite, the same model raises best-run success from 61.1% to 68.2% and average success from 47.1% to 53.6%. An ablation shows where the improvement actually comes from: skill construction alone (CSN-Gen plus CSN-Assess) accounts for 8.3 of the 10.4-point total gain on Qwen3.5-9B, while CSRL contributes the remaining 2.1. Most of the benefit is from generating and filtering better skills; reinforcement learning only refines at the margin.