Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step error amplification, prompt brittleness, and failures to revise incorrect commitments are difficult to explain by missing knowledge or expressive capacity alone. This su...
Jia-Zhang Cai, Tao Wang, Rui-Dong Zhang et al.· 0 citations
Reinforcement learning with verifiable rewards (RLVR) has significantly improved the reasoning capabilities of large vision-language models (LVLMs). However, standard on-policy RLVR algorithms face a critical optimization bottleneck in preserving and reinforcing visually grounded reasoning behaviors: valuable visually-...
Xin-Xin Song, Si-Yuan Li, Tingxiong Xiao et al.· 0 citations
CARE is proposed, which grounds every rubric evolution step in a high-quality anchor response generated by a frontier model conditioned on the prompt and its rubrics, enabling two complementary mechanisms: an Adaptive branch that reactively repairs reward misspecification; and a Chase branch that proactively converts f...
Si-Yuan Li, Xin-Xin Song, Rui-Nian Chen et al.· 0 citations
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