From the 1 of 6 linked papers with an AI index.
6 papers
Harness-G: A Graph-Structured Harness for Search Agents
Yanning Hou, Haoyuan Chen, Sihang Zhou +7
The paper introduces Harness-G, a graph-structured retrieval framework that turns free-form query generation into finite action selection for reinforcement learning search agents a…
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
Chenlu Ye, Zhou Yu, Ziji Zhang +5
Reinforcement Learning with Verifiable Rewards (RLVR) improves final-answer accuracy on reasoning tasks, but it does not reliably improve reasoning quality. Because outcome rewards…
Process Supervision via Verbal Critique Improves Reasoning in Large Language Models
Hao-Yuan Chen
Inference-time scaling for LLM reasoning has focused on three axes: chain depth, sample breadth, and learned step-scorers (PRMs). We introduce a fourth axis, granularity of externa…
Decoupled Travel Planning with Behavior Forest
Duanyang Yuan, Sihang Zhou, Yanning Hou +7
Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning s…
TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks
Vansh Kapoor, Aman Gupta, Hao Chen +3
Multi-step reasoning tasks like mathematical problem solving are vulnerable to cascading failures, where a single incorrect step leads to complete solution breakdown. Current LLM r…
Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism
Xiaoshu Chen, Sihang Zhou, Ke Liang +5
Chain of thought (CoT) fine-tuning aims to endow large language models (LLMs) with reasoning capabilities by training them on curated reasoning traces. It leverages both supervised…