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This work proposesSkill-$\alpha, a reinforcement learning method that learns a unified policy for progressive skill generation and introduces a novel rollback reward that evaluates each edit by comparing downstream execution under the original and edited skills on an anchored query.
This work makes the first attempt to build a medalist-level LLM agent for geometry and presents InternGeometry, which solves 44 of 50 IMO geometry problems (2000-2024), exceeding the average gold medalist score, using only 13K training examples, just 0.004% of the data used by AlphaGeometry 2.
This survey focuses on co-evolution in agentic systems, a multi-component form of self-evolution in which multiple agents and their environment impose adaptive pressure on one another.
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