2 citations · 3 across the 7 of their papers we have counts for
7 papers
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
Mengjie Ren, Jie Lou, Boxi Cao +6
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective paradigm for improving the reasoning capabilities of large language models. However, RLVR training…
Tackling Length Inflation Without Trade-offs: Group Relative Reward Rescaling for Reinforcement Learning
Zichao Li, Jie Lou, Fangchen Dong +8
Reinforcement learning significantly enhances LLM capabilities but suffers from a critical issue: length inflation, where models adopt verbosity or inefficient reasoning to maximiz…
The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model
Jiawei Chen, Wentao Chen, Jing Su +6
Large language models (LLMs) have shown significant multilingual capabilities. However, the mechanisms underlying the development of these capabilities during pre-training are not…
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation
Boxi Cao, Mengjie Ren, Hongyu Lin +4
Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which s…
Towards Scalable Automated Alignment of LLMs: A Survey
Boxi Cao, Keming Lu, Xinyu Lu +10
Alignment is the most critical step in building large language models (LLMs) that meet human needs. With the rapid development of LLMs gradually surpassing human capabilities, trad…
Learning or Self-aligning? Rethinking Instruction Fine-tuning
Mengjie Ren, Boxi Cao, Hongyu Lin +6
Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and th…
AI for social science and social science of AI: A Survey
Ruoxi Xu, Yingfei Sun, Mengjie Ren +5
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possib…