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cs.CL2026
Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification
Runa Yoshida, Kosuke Nishida, Kyosuke Nishida
The reasoning ability of large language models (LLMs) is a critical factor for practical LLM-based applications. To investigate the current reasoning capability of LLMs, we clarify…
cs.CL2026
Debiasing Reward Models via Causally Motivated Inference-Time Intervention
Kazutoshi Shinoda, Kosuke Nishida, Kyosuke Nishida
Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious features such as response leng…