SAW uses coefficient of variation to dynamically reweight objectives in MORL for LLMs, improving training efficiency and performance on tool-calling and summarization tasks under GRPO and GDPO.
Autagent: A reinforcement learning framework for tool-augmented audio reasoning
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Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.
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