5 papers · 1 filter
Simple-OPD: Demystifying Warm-up for On-policy Distillation
Tao Liu, Taiqiang Wu, Mao Zheng +5
On-policy distillation (OPD) trains a student on its own rollouts with token-level supervision from teacher models, but its effectiveness can depend strongly on the warm-up stage b…
GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs
Mingyang Song, Mao Zheng, Xuan Luo
Pairwise LLM-as-a-judge evaluation asks the judge to identify the \emph{better} of two candidate answers. We study a one-line modification that asks for the \emph{worse} answer ins…
FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models
Mingyang Song, Mao Zheng, Zheng Li +4
Improving training efficiency continues to be one of the primary challenges in large-scale Reinforcement Learning (RL). In this paper, we investigate how context length and the com…
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
Mingyang Song, Mao Zheng, Xuan Luo +1
Utilizing Large Language Models (LLMs) as evaluators to assess the performance of LLMs has garnered attention. However, this kind of evaluation approach is affected by potential bi…
Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models
Mingyang Song, Mao Zheng, Xuan Luo
Despite recent efforts to develop large language models with robust long-context capabilities, the lack of long-context benchmarks means that relatively little is known about their…