6 papers · 1 filter
FOCUS: Active Critic Learning for Long-Horizon Language Model Agents
Zhengping Jiang, William Jurayj, Liaoyaqi Wang +3
Sparse outcome rewards make it difficult for long-horizon language-model agents to assign credit to earlier actions. Learned critics provide value estimates for intermediate states…
Rethinking LoRA Memory Through the Lens of KV Cache Compression
Chunsheng Zuo, Liaoyaqi Wang, William Jurayj +2
Parametric retrieval augmentation encodes document information into lightweight, document-specific modules such as LoRA adapters, reducing the need to include all evidence as input…
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
Liaoyaqi Wang, Chunsheng Zuo, William Jurayj +2
Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability. Yet, outcome-based reward of…
A Brief Comparison of Training-Free Multi-Vector Sequence Compression Methods
Rohan Jha, Chunsheng Zuo, Reno Kriz +1
While multi-vector retrieval models outperform single-vector models of comparable size in retrieval quality, their practicality is limited by substantially larger index sizes, driv…
Multi-Vector Index Compression in Any Modality
Hanxiang Qin, Alexander Martin, Rohan Jha +3
We study efficient multi-vector retrieval for late interaction in any modality. Late interaction has emerged as a dominant paradigm for information retrieval in text, images, visua…
More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval
Chunsheng Zuo, Daniel Khashabi
Dense retrievers powered by pretrained embeddings are widely used for document retrieval but struggle in specialized domains due to the mismatches between the training and target d…