Language-model agents are usually trained by reinforcement learning from one reward per episode, and privileged self-distillation enriches it by letting the same policy, given a skill, teach its skill-free self through token probabilities. However, we identify two phenomena that question this channel. Invisible Advanta...
Mu-Yang Li, Jie Yang, Zheng-Yu Fang et al.· 1 citation· ⚡1
This work proposes HyperMCTS, a training-free method that augments an ordered MCTS tree with a cross-trajectory hypergraph, allowing outcomes collected under one prefix to inform selection under another while preserving execution histories in the tree.
GISAgentBench is introduced, a benchmark of 349 multi-step GIS tasks curated from GIS Stack Exchange and instantiated on real public data across six selected geographic areas of interest, enabling strict, deterministic, tolerance-aware output matching beyond LLM judging.
Abhinav Pothuri, Zhe Jiang, Ze-Lin Xu et al.· 0 citations
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