5 papers
Tracking the Moving Frontier: Long-Short Term Advantage Estimator
Xinhao Yao, Lu Yu, Changhao Wang +5
Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive and discarding useful experi…
GRIP: Geometric Refinement and Adaptive Information Potential for Data Efficiency
Changhao Wang, Jiaolong Yang, Xinhao Yao +7
The performance of Large Language Models (LLMs) is increasingly governed by data efficiency rather than raw scaling volume. However, existing selection methods often decouple globa…
Improving Autoformalization Using Direct Dependency Retrieval
Shaoqi Wang, Lu Yu, Siwei Lou +4
The convergence of deep learning and formal mathematics has spurred research in formal verification. Statement autoformalization, a crucial first step in this process, aims to tran…
The Debate on RLVR Reasoning Capability Boundary: Shrinkage, Expansion, or Both? A Two-Stage Dynamic View
Xinhao Yao, Lu Yu, Xiaolin Hu +4
The ongoing debate on whether reinforcement learning with verifiable rewards (RLVR) expands or shrinks the reasoning capabilities of large language models (LLMs) remains unresolved…
MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models
Jiazheng Li, Lu Yu, Qing Cui +4
High-quality data plays a critical role in the pretraining and fine-tuning of large language models (LLMs), even determining their performance ceiling to some degree. Consequently,…