most citedA Survey of Direct Preference Optimization

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Causal Improvement Graph for Agentic Harness Optimization

Junjie Zhang, Shunyu Liu, Haoyu Wang +3

Agentic Harness is the runtime that constructs task context and controls execution flow, thereby shaping overall agent performance. Given a fixed model and external evaluation, aut…

cs.LG2026

Entropy Pacing Policy Optimization for Multi-Task Agentic Reinforcement Learning

Zetian Hu, Shunyu Liu, Junjie Zhang +4

Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large Language Model (LLM) tasks. However, existing efforts largely focus on…

cs.LG2026

STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning

Junjie Zhang, Guozheng Ma, Shunyu Liu +5

Recent advances in Reinforcement Learning (RL) have underscored its potential for incentivizing reasoning capabilities of Large Language Models (LLMs). However, existing step-level…

cs.CL2025

A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models

Junjie Zhang, Guozheng Ma, Shunyu Liu +6

Reinforcement Learning with Verifiable Rewards~(RLVR) has emerged as a powerful learn-to-reason paradigm for large reasoning models to tackle complex tasks. However, the current RL…

cs.CL2025

Supervised Optimism Correction: Be Confident When LLMs Are Sure

Junjie Zhang, Rushuai Yang, Shunyu Liu +5

In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing…

cs.LG2025★ 2 cited

A Survey of Direct Preference Optimization

Shunyu Liu, Wenkai Fang, Zetian Hu +9

Large Language Models (LLMs) have demonstrated unprecedented generative capabilities, yet their alignment with human values remains critical for ensuring helpful and harmless deplo…