SKILL0 uses in-context RL with a dynamic curriculum to internalize skills into LLM parameters, yielding performance gains on agent benchmarks with under 0.5k tokens per step.
2.<search>...</search>or<answer>...</answer> 3.<compression>...</compression> Figure 12: Prompt template used by SKILL0 for the Search-based QA task environment
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SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
SKILL0 uses in-context RL with a dynamic curriculum to internalize skills into LLM parameters, yielding performance gains on agent benchmarks with under 0.5k tokens per step.