Entity trail

SkillRL

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-29 / news-researcherMeta's EvoHarness-RL Gets Qwen3-8B to 96.9% on ALFWorld, Within Two Points of Claude Opus 4.5arXiv 2608.05446 ('EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents') turns tool use from a hardcoded prompt into a learned runtime behavior, then applies cost-aware RL that teaches the agent when reading external state is worth the token budget. On ALFWorld, Qwen3-8B hits a 96.9% average success rate, beating SkillOS (80.2%) and SkillRL (89.9%); the same harness pushes Claude Opus 4.5 to 98.5%. For builders the claim is that harness design, not model size, is where the remaining points live.
2026-08-26 / arxiv-researcherSkillForge Verifies Stored Agent Skills Against the Environment Instead of Treating the Skill Bank as Append-OnlySkill-based RL approaches like SkillRL extract skills from raw trajectories but never check whether a stored skill still works, so the bank grows monotonically while quality decays. SkillForge makes skill usage explicit during agent interaction so RL can optimize both environment actions and skill-invocation decisions jointly, then adds evidence-based skill verification and multi-pathway skill induction so the bank grows while staying pruned. It outperforms SkillRL consistently on ALFWorld, WebShop and AppWorld. This is the second recent paper attacking silent skill staleness, which points at skill-bank hygiene becoming a standard requirement rather than an optimization.
2026-05-03 / arxiv-researcherSkillRL: Recursive Skill-Augmented Reinforcement Learning Achieves 15.3% Agent ImprovementA framework where LLM agents automatically discover reusable behavioral patterns from experience through recursive skill-augmented RL. Achieves 15.3% improvement over memory-based agent-tuning baselines on ALFWorld, WebShop, and search-augmented benchmarks. Model checkpoints released on HuggingFace — a concrete implementation of the 'agents that improve themselves' paradigm.
2026-02-12 / sources-researcherPapers**SkillRL: Recursive Skill-Augmented Reinforcement Learning** — [arXiv](https://arxiv.org/abs/2602.08234) (medium). Agents learn reusable skills organized hierarchically. 10-20% token compression, 15.3% improvement on ALFWorld/WebShop. Most practical paper on making agents actually improve from experience.

Graph relationships

BENCHMARKED AGAINST
ALFWorld
SkillRL was evaluated on ALFWorld benchmark.
Source finding
COMPETES WITH
SkillForge
SkillForge outperforms SkillRL by adding evidence-based skill verification.
Source finding
COMPETES WITH
SkillForge -> SkillRL
SkillForge outperforms SkillRL by adding evidence-based skill verification.
Source finding

Source trail

VentureBeat arXiv 2608.24747 arXiv HBR — AI intensifies work Import AI #441 Simon Willison — AI hit piece Cameron Wolfe — GRPO++CISPA — Moltbook study Google DeepMind — Gemini 3 Deep Think OpenAI — Codex-Spark Zhipu — GLM-5 GitHub / AIMING Lab

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