5 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective
Han Cui, Jianhao Yan, Yun Luo +3
On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also collapse into excessively long a…
Semifactual Credit-Augmented Policy Optimization
Junshu Pan, Zhizhang Fu, Shulin Huang +5
Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions remain sensitive to task-irrele…
Can Retrievers Find the Same Paper from Different Aspects? A Multi-Aspect Full-Paper Scientific Retrieval Benchmark
Yiyang Wei, Fang Guo, Qiji Zhou +4
Scientific papers contain multiple searchable facets such as background, methods. However, many paper retrieval benchmarks merely evaluate individual query-paper relevance, while o…
ReEfBench: Quantifying the Reasoning Efficiency of LLMs
Zhizhang Fu, Yuancheng Gu, Chenkai Hu +2
Test-time scaling has enabled Large Language Models (LLMs) to tackle complex reasoning, yet the limitations of current Chain-of-Thought (CoT) evaluation obscures whether performanc…