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cs.CL2026
TIAR: Trajectory-Informed Advantage Reweighting for LLM Abstention Learning
Muyu Pan, Shu Zhao, Nan Zhang +4
This paper investigates large language model (LLM) abstention learning, specifically using ternary reward, which incentivize truthfulness in large language models. This paper exten…
cs.LG2026
Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road
Ngoc-Hieu Nguyen, Parshin Shojaee, Phuc Minh Nguyen +4
Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex tasks through specialized fine-tuning procedur…
cs.LG2026
QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
Nan Zhang, Eugene Kwek, Yusen Zhang +4
Weight-only quantization is important for compressing Large Language Models (LLMs). Inspired by the spirit of classical magnitude pruning, we study whether the magnitude of weight…