1 citations · 4 across the 9 of their papers we have counts for
10 papers
Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling
Siyuan Gan, Yuhan Li, Xiran Wang +6
Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) is a prominent variant of Group Relative Policy Optimization (GRPO). DAPO introduces several improvements over GRPO.…
When Teacher Guidance Misleads: Reward-Aligned On-Policy Distillation
Siyuan Gan, Yuhan Li, Xiran Wang +5
On-policy distillation (OPD) has recently emerged as a popular post-training paradigm for large language models (LLMs), providing an efficient way to transfer the knowledge and cap…
Coarse-to-Control: Action-Token Planning for Vision-Language-Action Models
Jinhao Wu, Shiduo Zhang, Yicheng Liu +9
Most vision-language-action (VLA) models map observations directly to actions without explicit intermediate planning, which limits performance on long-horizon tasks where early mis…
Efficient Reinforcement Learning with Semantic and Token Entropy for LLM Reasoning
Hongye Cao, Zhixin Bai, Ziyue Peng +5
Reinforcement learning with verifiable rewards (RLVR) has demonstrated superior performance in enhancing the reasoning capability of large language models (LLMs). However, this acc…
Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning
Siyuan Gan, Jiaheng Liu, Boyan Wang +8
Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (Co…
Multi-Agent Reinforcement Learning with Communication-Constrained Priors
Guang Yang, Tianpei Yang, Jingwen Qiao +4
Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems. However, in most real-world scenarios, lossy communication is a pr…
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation
Hongye Cao, Fan Feng, Jing Huo +4
Model-based offline Reinforcement Learning (RL) constructs environment models from offline datasets to perform conservative policy optimization. Existing approaches focus on learni…
Causal Information Prioritization for Efficient Reinforcement Learning
Hongye Cao, Fan Feng, Tianpei Yang +2
Current Reinforcement Learning (RL) methods often suffer from sample-inefficiency, resulting from blind exploration strategies that neglect causal relationships among states, actio…
Towards Empowerment Gain through Causal Structure Learning in Model-Based RL
Hongye Cao, Fan Feng, Meng Fang +4
In Model-Based Reinforcement Learning (MBRL), incorporating causal structures into dynamics models provides agents with a structured understanding of the environments, enabling eff…
SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks
Hongye Cao, Sijia Jing, Yanming Wang +14
With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on…