1 citations · 4 across the 36 of their papers we have counts for
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HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
Yucan Guo, Xiaohan Wang, Miao Su +8
Tool-Integrated Reasoning (TIR) is a fundamental capability for LLM agents to solve complex tasks by interacting with external tools iteratively. Reinforcement Learning (RL) has be…
When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents
Zihan Lin, Zhenyu Chen, Jiawen Wei +6
Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents. Prevailing agent self-evolution paradigms typically rely on a co…
UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams
Siyu Xia, Chenheng Zhang, Yanting Wu +8
Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving ta…
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
Are Full Rollouts Necessary for On-Policy Distillation?
Yaocheng Zhang, Jiajun Chai, Yuqian Fu +7
On-policy distillation (OPD) provides dense teacher feedback along student-generated rollouts rather than fixed teacher traces and has emerged as a promising post-training paradigm…
On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
Xiaotian Ye, Xiaohan Wang, Mengqi Zhang +1
Counterfactual tuning (CFT) has emerged as a promising paradigm for Large Language Model (LLM) unlearning by training models to generate alternative fictitious knowledge in place o…
Implicit Hierarchical GRPO: Decoupling Tool Invocation from Execution for Tool-Integrated Mathematical Reasoning
Li Wang, Xiaohan Wang, Xiaodong Lu +5
Large language models (LLMs) have increasingly leveraged tool invocation to enhance their reasoning capabilities. However, existing approaches typically tightly couple tool invocat…
Rethinking Personalization in Large Language Models at the Token Level
Chenheng Zhang, Yijun Lu, Lizhe Fang +7
With large language models (LLMs) now performing strongly across diverse tasks, there is growing demand for them to personalize outputs for individual users. Personalization is typ…
AWPO: Enhancing Tool-Use of Large Language Models through Adaptive Integration of Reasoning Rewards
Zihan Lin, Xiaohan Wang, Hexiong Yang +5
While Reinforcement Learning (RL) shows promise in training tool-use Large Language Models (LLMs) using verifiable outcome rewards, existing methods largely overlook the potential…
From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory
Siyu Xia, Zekun Xu, Jiajun Chai +7
Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving across complex, open-ended environments. A promising approach for improv…
ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models
Zihan Lin, Xiaohan Wang, Jie Cao +4
Large language models (LLMs) transcend passive generation and act as goal-directed agents by invoking external tools. Reinforcement learning (RL) offers a principled framework for…