From the 1 of 13 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
13 papers
EviSD: Evidence-Conditioned Self-Distillation for Search-Augmented Agents
Jianan Xie, Xin Sun, Zhongqi Chen +4
Outcome-based reinforcement learning enables search-augmented language agents to learn from verifiable final answers, but its trajectory-level credit cannot distinguish the contrib…
HopRefusalBench: Diagnosing Refusal Failures in Search-Augmented Agents for Multi-Hop Reasoning
Jianan Xie, Xin Sun, Zhongqi Chen +3
Search-augmented large language model agents are increasingly capable of solving knowledge-intensive tasks, but their behavior when a multi-hop question is fundamentally unanswerab…
Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation
Xin Sun, Zhongqi Chen, Qiang Liu +5
The paper introduces TTARAG, a test-time adaptation technique that updates a language model's parameters during inference to better integrate retrieved knowledge for specialized do…
KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering
Xin Sun, Zhongqi Chen, Xing Zheng +6
Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generating executable logical forms. Whi…
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…
GeoMin: Data-Efficient Semi-Supervised RLVR via Geometric Distribution Modeling
Guangcheng Zhu, Shenzhi Yang, Haobo Wang +9
Reinforcement learning with verifiable rewards (RLVR) significantly advances LLM reasoning, yet it faces a dilemma: standard supervised scaling is throttled by high annotation cost…