20 papers
Token-Level Credit Assignment Optimization for Generative Document Retrieval
Xinpeng Zhao, Yang Liu, Ran Chen +6
Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, whe…
OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning
Ziyou Hu, Zhengliang Shi, Minghang Zhu +5
Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both training and inference. However, exist…
Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
Zhao Yang, Yuxuan Jiang, Ting-Chih Chen +18
Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a narrow slice of what RL offers.…
Disentangling Knowledge Representations for Large Language Model Editing
Mengqi Zhang, Zisheng Zhou, Xiaotian Ye +4
Knowledge Editing has emerged as a promising solution for efficiently updating embedded knowledge in large language models (LLMs). While existing approaches demonstrate effectivene…
SA-CAISR: Stage-Adaptive and Conflict-Aware Incremental Sequential Recommendation
Xiaomeng Song, Xinru Wang, Hanbing Wang +4
Sequential recommendation (SR) aims to predict a user's next action by learning from their historical interaction sequences. In real-world applications, these models require period…
Curriculum Approximate Unlearning for Session-based Recommendation
Liu Yang, Zhaochun Ren, Ziqi Zhao +7
Approximate unlearning for session-based recommendation refers to eliminating the influence of specific training samples from the recommender without retraining of (sub-)models. Gr…