5 papers
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
Xiaojun Wu, Xiaoguang Jiang, Huiyang Li +11
Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving. Recent methods have improved reason…
NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research
Qiaobo Hao, Yangqian Wu, Shunyi Wang +5
Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold detailed data construction,…
TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
Jieting Xiao, Yun Lin, Huizhen Qiu +10
While Large Language Models have achieved remarkable integration in various vertical scenarios, their deployment in the telecommunications domain remains exploratory due to the lac…
Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning
Taojie Zhu, Dongyang Xu, Ding Zou +4
Post-training paradigms for Large Language Models (LLMs), primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dilemma: SFT provides stability…
CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C++ Compilation Repair
Weixuan Sun, Jucai Zhai, Dengfeng Liu +6
The automated repair of C++ compilation errors presents a significant challenge, the resolution of which is critical for developer productivity. Progress in this domain is constrai…