5 papers · 1 filter
From Final Artifacts to Trajectories: Retrospective Process Supervision for Evidence-Grounded Long-Form Generation
Junjie Huang, Jiarui Qin, Di Yin +4
Trajectory data is getting more vital for training large language models for boosting the agentic abilities. Unlike the verifiable domains such as coding or mathematics, scaling tr…
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Zheng Yuan, Chuang Zhou, Linhao Luo +4
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably intro…
ReMiT: RL-Guided Mid-Training for Iterative LLM Evolution
Junjie Huang, Jiarui Qin, Di Yin +4
Standard training pipelines for large language models (LLMs) are typically unidirectional, progressing from pre-training to post-training. However, the potential for a bidirectiona…
PoLi-RL: A Point-to-List Reinforcement Learning Framework for Conditional Semantic Textual Similarity
Zixin Song, Bowen Zhang, Qian-Wen Zhang +3
Conditional Semantic Textual Similarity (C-STS) measures the semantic proximity between text segments under a specific condition, thereby overcoming the ambiguity inherent in tradi…
CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity
Bowen Zhang, Zixin Song, Chunquan Chen +3
Learning unified text embeddings that excel across diverse downstream tasks is a central goal in representation learning, yet negative transfer remains a persistent obstacle. This…