SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

Key Takeaways

• SKILL0 internalizes skills, eliminating runtime retrieval
• Dynamic curriculum drops skill text as model learns
• Achieves 9.7% gain on ALFWorld, 6.6% on Search‑QA
• Reduces context length to under 500 tokens per step
• Maintains zero‑shot performance without external hints

Pulse Analysis

SKILL0 tackles a core inefficiency in current LLM agents: the reliance on external skill prompts that bloat the context window and introduce retrieval noise. By treating skill acquisition as a reinforcement learning objective, the framework teaches the model to absorb tool usage and multi‑step reasoning into its weights. This shift mirrors how humans internalize procedural knowledge, moving from step‑by‑step instructions to autonomous execution, and it opens the door for more compact, cost‑effective deployments in production environments.

The dynamic curriculum at the heart of SKILL0 continuously evaluates the marginal benefit of each skill description during training. When performance on a validation task remains stable without a particular skill, the system prunes that text, gradually reducing the reliance on in‑context guidance. This adaptive pruning not only accelerates convergence but also ensures that the final model can operate with a minimal token footprint—under 500 tokens per inference step—significantly lowering compute overhead for cloud‑based services.

Empirical results underscore SKILL0’s practical impact. In the ALFWorld simulation, the model outperforms strong reinforcement learning baselines by 9.7%, while in the Search‑QA domain it registers a 6.6% lift. These gains, combined with the streamlined context, translate into faster response times and reduced API costs for enterprises leveraging LLMs for autonomous agents, customer support bots, or data‑driven decision tools. As organizations scale AI‑driven workflows, frameworks like SKILL0 will be pivotal in balancing performance with operational efficiency.