4 papers
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
Teng Chen, Sheng Xu, Feixiang Guo +4
Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial…
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
Zhaowei Liu, Xin Guo, Zhi Yang +14
In recent years, general-purpose large language models (LLMs) such as GPT, Gemini, Claude, and DeepSeek have advanced at an unprecedented pace. Despite these achievements, their ap…
Trade-R1: Bridging Verifiable Rewards to Stochastic Environments via Process-Level Reasoning Verification
Rui Sun, Yifan Sun, Sheng Xu +5
Reinforcement Learning (RL) has enabled Large Language Models (LLMs) to achieve remarkable reasoning in domains like mathematics and coding, where verifiable rewards provide clear…
Logits-Constrained Framework with RoBERTa for Ancient Chinese NER
Wenjie Hua, Shenghan Xu
This paper presents a Logits-Constrained (LC) framework for Ancient Chinese Named Entity Recognition (NER), evaluated on the EvaHan 2025 benchmark. Our two-stage model integrates G…