Yiju Guo

PhD Student

Gaoling School of Artificial Intelligence, Renmin University of China

Research Interests

LLM Alignment & Reasoning

Reinforcement Learning

Agentic Reinforcement Learning

About

I am a PhD student at Gaoling School of Artificial Intelligence, Renmin University of China (RUC), working in the RUCBM lab and advised by Prof. Yankai Lin. I am also conducting research at Natural Language Processing Lab at Tsinghua University(THUNLP), supervised by Prof. Ning Ding. Currently, I am interning at ByteDance's Seed Foundation Model Team.

My research focuses on Large Language Model (LLM) and Reinforcement Learning (RL). Specifically, I am interested in:

LLM Alignment (e.g., RLHF, Multi-Objective Optimization)
Reasoning & Generation (e.g., Causal Inference, Attention Mechanisms)
Agentic Reinforcement Learning (e.g., Multi-Turn Interaction, Experience Learning)

Selected Publications

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Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification

Yiju Guo, Tianyi Hu, Zexu Sun, Yankai Lin

ACL 2026 Conference

An RLVR framework that boosts sampling success by pruning prompt interference tokens, achieving faster convergence and improved performance.

Learning to Focus: Causal Attention Distillation via Gradient-Guided Token Pruning

Yiju Guo, Wenkai Yang, Zexu Sun, Ning Ding, Zhiyuan Liu, Yankai Lin

NeurIPS 2025 Conference

A framework to improve LLM reasoning by removing distracting tokens via causal attention distillation and gradient-guided pruning.

Controllable preference optimization: Toward controllable multi-objective alignment

Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, others

EMNLP 2024 main conference

A multi-objective alignment method that explicitly controls preference scores to balance helpfulness, honesty, and harmlessness.

Uncertainty and influence aware reward model refinement for reinforcement learning from human feedback

Zexu Sun, Yiju Guo, Yankai Lin, Xu Chen, Qi Qi, Xing Tang, Ji-Rong Wen

ICLR 2025 Conference

An uncertainty-aware data augmentation method to refine reward models in RLHF without expensive human annotation.

LaSeR: Reinforcement Learning with Last-Token Self-Rewarding

Wenkai Yang, Weijie Liu, Ruobing Xie, Yiju Guo, Lulu Wu, Saiyong Yang, Yankai Lin

ICLR 2026 Conference

An efficient RL method that unifies reasoning and verification by utilizing the last-token probability as a self-rewarding signal.

News

2026-04

Our work "Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification" has been accepted to ACL 2026 🎉

2026-01

Our work "LaSeR: Reinforcement Learning with Last-Token Self-Rewarding" has been accepted to ICLR 2026 🎉

2025-09

Our work "Learning to Focus: Causal Attention Distillation via Gradient-Guided Token Pruning" has been accepted to NeurIPS 2025 (main) 🎉

2025-01

Our work "Uncertainty and Influence-Aware Reward Model Refinement for Reinforcement Learning from Human Feedback" has been accepted to ICLR 2025 🎉

2024-09

Our work "Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment" has been accepted to EMNLP 2024 (main) 🎉

2022-09

Started my PhD at Gaoling School of Artificial Intelligence, Renmin University of China (RUC)