TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models
This work presents TEMPO (Temporally-grounded Multi-task Post-training), the first unified model to handle audio, speech, and music timestamping tasks and introduces the first application of reinforcement learning to unified audio timestamping, using GRPO with verifiable temporal rewards that directly optimize the evaluation objectives.
Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh et al.
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