LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training

arXiv:2606.07610v1 Announce Type: new Abstract: State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same terminal-reward advantage to every token in a response. This ignores useful structure within rollout batches, where speech-conditioned completions often share prefixes before diverging at important decisions. We propose Low-rank Exploration with Adaptive Forking (LEAF), a retrospective tree-based RL method that recovers this structure without online branching or additional decoding. LEAF samples compl

Source: arXiv cs.LG — read the full report at the original publisher.

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