31 citations · 74 across the 63 of their papers we have counts for
11 papers · 1 filter
BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation
Ning Li, Zixuan Guo, Yan Xu +7
Hallucinations remain a major obstacle to deploying large language models (LLMs) in knowledge-intensive settings, where generated responses must be faithfully grounded in provided…
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents
Haoyi Hu, Qirong Lyu, Xianghan Kong +7
While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This par…
Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents
Jingxing Wang, Chenyu Zhou, Zhihui Fu +4
Additional test-time compute can give LLM agents access to more past experience, yet expanding the context or adding rollouts does not necessarily yield greater agent capability. W…
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…
LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
Kangning Zhang, Wenxiang Jiao, Kounianhua Du +4
Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipel…
Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
Xinda Jia, Jinpeng Li, Zezhong Wang +6
Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting the reaso…