arXiv:2603.00578v2 Announce Type: replace-cross
Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial...
By Jie Cao, Tianwei Lin, Juekai Lin, Zhenxuan Fan, Yunqi Cao, Bo Yuan, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Siliang Tang
arXiv:2603.00889v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforce...
By Xinyu Zhu, Yihao Feng, Yanchao Sun, Xianzhi Du, Pingzhi Li, Olli Saarikivi, Yun Zhu, Yu Meng
CoTranslator is a method that converts reverse chain-of-thought (CoT) responses into verified forward CoT responses, enabling efficient large-scale generation and verification of training data for reasoning distillation. Trained on a small set of paired reverse and rejection-sampled forward responses, it improves student model accuracy on mathematics benchmarks by up to 5.82 percentage points and retains 95.0% of rejection sampling’s accuracy while using only 35.9% of its compute. The translator also transfers to new datasets, teachers, and STEM problems, boosting average STEM accuracy for 4B and 8B students by 4.88 and 4.31 percentage points, respectively.
By Zinan Tang, Zhuoshi Pan, Kairui Chen, Ze Yan, Shaomian Zheng, Yukun Zhang, Gang Wang, Biqing Huang
Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards. Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even when the final solution is correct, an imperfect rationale can interfere with learning.
arXiv:2606. 01682v1 Announce Type: cross Abstract: Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to incorrect reasoning paths.
By Atoosa Chegini, Soheil Feizi
arXiv:2604.02341v2 Announce Type: replace-cross
Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final...
By Mohammad Rezaei, Jens Lehmann, Sahar Vahdati