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Full-duplex speech large language models (LLMs) enable low-latency, natural voice interaction. However, real-world agents must also use tools and perform deliberative reasoning-operations whose variable latency and computational cost conflict with the stringent timing requirements of real-time conversation. To reconcil...
This work introduces Few-Shot Aware GRPO (FSA-GRPO), an RL-based post-training recipe that uses a specially designed reward to encourage the model to leverage few-shot demonstrations, thereby strengthening its few-shot adaptation ability.
A multi-layer feature fusion (MLF) adapter that aggregates information from all encoder layers before projecting them into the language model is proposed and shows that MICL does not emerge naturally in ALLMs, but can be effectively acquired through targeted contextual biasing training.
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