AI

SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 04:18:24 UTC.

Briefings

SkillForge is an agentic reinforcement learning method that co-evolves skills with LLMs through a dynamic lifecycle of trial, active, stable, and retired states. It achieves the highest aggregate success rate in interactive benchmarks, delivering up to 7.8% relative improvement over the strongest baseline while maintaining a compact skill library. The system uses a pre-RL evaluation phase to filter low-fitness skills before supervised fine-tuning, followed by iterative RL optimization with selective retirement and LLM-guided mutation.

Key points

• SkillForge uses a dynamic skill lifecycle with four states: trial, active, stable, and retired, allowing skills to co-evolve with the model.
• The method achieves up to 7.8% relative improvement over the strongest baseline in interactive agent benchmarks.

Context

SkillForge addresses the problem of accumulating obsolete or harmful skills in memory-augmented reinforcement learning. By using a fitness-driven lifecycle to filter and evolve skills, the system ensures the skill library remains compact and effective throughout training, improving upon prior methods that retained all skills indiscriminately.

Limitations

The evidence is limited to a single preprint from October 2026. Results are based on interactive agent benchmarks and pre-RL evaluation data. No long-term performance data, failure rates, or real-world deployment metrics are provided. The 7.8% improvement is relative to a baseline and may vary across different benchmark configurations.

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