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On-policy distillation post-trains large language model agents by supplying dense, step-level guidance from a teacher policy when the reinforcement-learning reward is sparse and arrives only once per trajectory. Existing instantiations allocate this guidance by the size of the teacher-student divergence at each step, o...
Long-horizon search requires agents to gather evidence across multiple steps and synthesize it into well-supported answers. The recent agent harnesses provide a natural and promising framework to support such long-running search processes. As interaction histories grow, one single agent in harnesses might get stuck and...
Detailed image captioning requires accurate and comprehensive descriptions of fine-grained visual content, yet caption quality spans factual accuracy, information coverage, and clarity. Compared with conventional methods that rely mainly on high-quality supervision or holistic rewards, rubric-based reinforcement learni...
GS-IQA is proposed, a framework that reformulates IQA as a progressive Where--What--How diagnosis, emulating the human perceptual process from an initial glance to closer scrutiny, and consistently surpasses state-of-the-art methods in distortion localization, recognition, and severity estimation.
RING (Retrieval-Internalized Generation), a holistic paradigm spanning both architecture and training that injects large-scale external knowledge into aixture-of-Memory Experts and learns parametric search over this internal memory via reinforcement learning, removing the external retriever entirely, is proposed.
A training-free, Adaptive Visual Token Pruning (AVTP) framework, applicable to diverse LVLM architectures is proposed, and adaptive pruning ratios in multi-image contexts where images of higher importance retain proportionally more tokens are implemented.
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