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The conclusion is not that LLM rerankers are robust, but that negative distributions are part of the retriever-reranker interface and should be checked before deployment.
The conclusion is not that LLM rerankers are robust, but that negative distributions are part of the retriever-reranker interface and should be checked before deployment.
Reinforcement learning with verifiable rewards (RLVR) controls policy entropy by reweighting tokens against an external scalar advantage. On-policy distillation (OPD) faces the same surface problem, a student that can sharpen too fast, but it replaces that advantage with a per-token teacher target. That shared symptom...
Large Audio Language Models (LALMs) utilize either continuous features or discrete tokens, yet the optimal representation paradigm for general audio understanding remains debated. Existing benchmarks often focus on narrow domains or evaluate encoders outside LALM contexts. To address these gaps, we systematically evalu...
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