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Memory-augmented reinforcement learning strengthens LLM agents'ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions with an applicability condition over task types. However, retaining every skill indiscriminately as the policy improves lets obsolete or harmful entries ac...
This work introduces Audio-Zero, the first label-free self-evolution framework in the field of LALMs that improves fine-grained auditory perception and reasoning and reveals that increasingly fine-grained auditory descriptions emerge naturally from game pressure.
This work introduces Polarity-Prompt Contrastive Decoding (PopCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings and is applicable to both LLMs and Vision-Language Models without additional training.
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