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Multimodal large language models (MLLMs) struggle with spatial, embodied, physical, and temporal reasoning. We hypothesize that these failures reflect a shared deficit in visual transition reasoning, and test whether this capability can serve as a shared training primitive, one that different models can learn from diff...
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.
Cliff, a reward shaping strategy that utilizes an off-the-shelf LLM as a teacher to identify the first mistake in each rollout, is proposed and established as a simple, general and effective approach for improving RLVR with richer, fine-grained supervision.
This work proposes VEG (verbal ϵ -greedy), a novel framework that leverages external feedback as a dynamic control variable to explicitly balance exploration and exploitation within the semantic space and achieves superior accuracy compared to standard RL baselines.
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