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Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment across long interaction sequences. Recent work on on-policy self-distillation (OPSD) offers complementary supervision by evaluating a policy's...
Tree-structured reinforcement learning trains search agents by comparing alternative continuations and propagating terminal rewards to intermediate decisions. Adaptive expansion, however, creates a statistical asymmetry: an incumbent is selected using its own generation statistic, whereas fresh siblings are sampled aft...
Self-Supervised Skill Optimization (SSO) is introduced, a comparative framework that learns a reusable skill from unlabeled task instances alone and outperforms existing GT-free prompt optimizers on both closed-ended and open-ended tasks.
WebRetriever is introduced, a large-scale benchmark encompassing 800 websites and 1,550 tasks across diverse domains, including consumer, professional, and enterprise sectors, with comprehensive coverage of user intent patterns, and NavEval (Navigation Evaluation), a novel LLM-as-Judge framework that leverages rich int...
This work systematically characterize the implicit biases introduced by low-rank adaptation during alignment and establishes two theorems showing that low-rank alignment induces preferences for parameter subspaces with flat gradients and feature subspaces robust to perturbations, providing a principled explanation for...
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