2 citations · 2 across the 6 of their papers we have counts for
6 papers
CircuitLens: Reasoning Circuits as Data Selection Signals for Reinforcement Learning with Verifiable Rewards
Zhuofan Chen, Ziqian Jiao, Yikai Cui +3
Reinforcement learning with verifiable rewards (RLVR) is sensitive to which problems a model trains on, yet existing selection criteria--difficulty filtering, hand-curation, reward…
Intuition-Guided Latent Reasoning for LLM-Based Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender syst…
Xetrieval: Explaining Dense Retrieval through Feature Elicitation
Zhixin Cai, Jun Bai, Yang Liu +7
Dense retrieval enables effective semantic matching, yet the semantic factors underlying its relevance scores remain opaque. Sparse autoencoders (SAEs) support feature-level explan…
Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation
Yimeng Bai, Chang Liu, Yang Zhang +5
Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically invol…
Enhancing LLMs via High-Knowledge Data Selection
Feiyu Duan, Xuemiao Zhang, Sirui Wang +4
The performance of Large Language Models (LLMs) is intrinsically linked to the quality of its training data. Although several studies have proposed methods for high-quality data se…
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Haoran Que, Feiyu Duan, Liqun He +11
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks (e.g., long-context understanding), and many benchmarks have been proposed.…