1 citations · 1 across the 14 of their papers we have counts for
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Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning
Ang Li, Yue Lin, Feifei Kou +6
An LLM agent can recognize uncertainty yet still choose the wrong next step: asking when action is already justified, or seeking clarification when the constraints must change. We…
TAPO: Transition-Aware Policy Optimization for LLM Agents
Cong Li, Peixi Peng, Yisen Zhao +4
Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents. However, existing methods predominantly rely on…
Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning
Jia Ao Sun, Hao Yu, Fengran Mo +4
Knowledge graph question answering (KGQA) requires navigating from topic entities to an answer several relations away. Recent methods prompt a frontier LLM to explore the graph thr…
Learning to Select, Not Relearn: Hard-Routed Mixtures of Reasoning LoRAs
Seyed Alireza Molavi, Zhan Su, Yan Hu +3
Composing independently trained LoRA adapters into a single large language model is useful for multi-domain adaptation, especially when the original training data cannot be shared.…
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
Chenjun Xu, Zhennan Zhou, Zhan Su +3
Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is especially problematic in low-data fi…
Efficient Data Selection for Multimodal Models via Incremental Optimization Utility
Jinhao Jing, Qiannian Zhao, Chao Huang +1
The scaling of Large Multimodal Models (LMMs) is constrained by the quality-quantity trade-off inherent in synthetic data. Previous approaches, such as LLM-as-a-Judge, have proven…
Towards Dynamic Dense Retrieval with Routing Strategy
Zhan Su, Fengran Mo, Jinghan Zhang +4
The \textit{de facto} paradigm for applying dense retrieval (DR) to new tasks involves fine-tuning a pre-trained model for a specific task. However, this paradigm has two significa…
OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG
Fengran Mo, Zhan Su, Yuchen Hui +6
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The qual…
COVR:Collaborative Optimization of VLMs and RL Agent for Visual-Based Control
Canming Xia, Peixi Peng, Guang Tan +4
Visual reinforcement learning (RL) suffers from poor sample efficiency due to high-dimensional observations in complex tasks. While existing works have shown that vision-language m…