Description
Formulates agent skill optimization as a bilevel problem: outer loop = MCTS over skill structure (which instructions/tools to include), inner loop = LLM-guided refinement of component content within the chosen structure. MCTS uses delayed feedback to balance exploitation and exploration of edit paths.
Relevance to Zeph
zeph-skills has a self-learning path (hot-reload, embedding evolution) but no principled structure-space search. Bilevel MCTS could drive autonomous skill restructuring in response to performance signals from zeph-experiments.
Proposed Action
Evaluate MCTS-guided skill structure optimization as a post-hoc improvement layer on top of the existing SkillEvolution pipeline. Start with a small skill library and measure quality delta.
Reference
Description
Formulates agent skill optimization as a bilevel problem: outer loop = MCTS over skill structure (which instructions/tools to include), inner loop = LLM-guided refinement of component content within the chosen structure. MCTS uses delayed feedback to balance exploitation and exploration of edit paths.
Relevance to Zeph
zeph-skills has a self-learning path (hot-reload, embedding evolution) but no principled structure-space search. Bilevel MCTS could drive autonomous skill restructuring in response to performance signals from zeph-experiments.
Proposed Action
Evaluate MCTS-guided skill structure optimization as a post-hoc improvement layer on top of the existing SkillEvolution pipeline. Start with a small skill library and measure quality delta.
Reference