407 citations · 489 across the 48 of their papers we have counts for
15 papers · 1 filter
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning
Guangcheng Zhu, Yingfan Ma, Xing Zheng +7
Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large language models on open-ended task…
PreSO: Privileged Representation Isolation for On-Policy Self-Distillation
Yuhang Yang, Mingxuan Xia, Chao Ye +6
On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning with verifiable rewards (RL…
DE-Venus: A Data-Efficient RLVR Framework for Large Language Models
Shenzhi Yang, Guangcheng Zhu, Kai Tang +11
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-policy rollouts and the cost…
Frozen Cores Need Task Signal: Fisher-Whitened Cross-Covariance for Low-Resource LLM Adaptation
Wentao Ye, Zhanming Shen, Zhiqing Xiao +3
Parameter-efficient fine-tuning is usually framed as a question of how many parameters to update. Under a severe trainable-state budget, however, where those coefficients act is eq…
CriPO: Enhancing Rubric-based RL via Self-Distillation
Mingxuan Xia, Yuhang Yang, Chao Ye +8
Rubric-based Reinforcement Learning (RL) has recently shown promise in improving Large Language Models (LLMs) on open-ended tasks. A widely recognized limitation of rubric-based RL…
Semantically Similar, Yet Not Answerable: Diagnosing the Semantic-Answerability Gap in Table RAG
Jiaming Tian, Liyao Li, Wentao Ye +6
In retrieval-augmented generation (RAG), semantic relevance asks whether a source matches a query in meaning, while answerability asks whether it contains sufficient information to…
Purified OPSD: On-Policy Self-Distillation Without Losing How to Think
Zhanming Shen, Jintao Tong, Shaotian Yan +9
On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference solutions provides token-lev…
OPRD: On-Policy Representation Distillation
Shenzhi Yang, Guangcheng Zhu, Bowen Song +8
On-policy distillation (OPD) supervises the student exclusively in the output space by matching next-token distributions. This paradigm suffers from two limitations: (i) a high-var…
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
Hao Chen, Zhanming Shen, Liyao Li +8
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods base…
SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization
Qi Zhang, Zhaopeng Feng, Xiaonan Shi +8
Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, cur…
DeltaMem: Towards Agentic Memory Management via Reinforcement Learning
Qi Zhang, Shen Huang, Chu Liu +4
Recent advances in persona-centric memory have revealed the powerful capability of multi-agent systems in managing persona memory, especially in conversational scenarios. However,…
FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning
Ziquan Wang, Haobo Wang, Ke Chen +2
Machine Learning often involves various imprecise labels, leading to diverse weakly supervised settings. While recent methods aim for universal handling, they usually suffer from c…
Stop Unnecessary Reflection: Training LRMs for Efficient Reasoning with Adaptive Reflection and Length Coordinated Penalty
Zewei Yu, Lirong Gao, Yuke Zhu +4
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks by employing test-time scaling. However, they often generate over-long chains-of-t…
A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models
Zhengqing Zang, Yuqi Ding, Yanmei Gu +5
Human logic has gradually shifted from intuition-driven inference to rigorous formal systems. Motivated by recent advances in large language models (LLMs), we explore whether LLMs…
Table as a Modality for Large Language Models
Liyao Li, Chao Ye, Wentao Ye +9
To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to generalize them to the table reasoning tasks for the widely deployed…