1 citations · 1 across the 13 of their papers we have counts for
14 papers
CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition
Lai Wei, Chengqi Li, Jiapeng Li +3
Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as co…
Distilled Reinforcement Learning for LLM Post-training
Chen Wang, Zhaochun Li, Jionghao Bai +4
Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment. Existing methods mainly follow two paradigms: reinforcement learning (RL)…
HTAM: Hierarchical Transition-Attended Memory for Operator Optimization
Yining Zhang, Mingyang Yi, Chen Wang +5
High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generation makes automatic GPU opera…
Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training
Chen Wang, Hexuan Deng, Yining Zhang +5
Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning traces. Existing methods ma…
Attend to Evidence: Evidence-Anchored Spatial Attention Supervision for Multimodal RLVR
Ruina Hu, Chen Wang, Lai Wei +5
Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only re…
Targeted Exploration via Unified Entropy Control for Reinforcement Learning
Chen Wang, Lai Wei, Yanzhi Zhang +5
Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs). However, the widely used…
Belief-Aware VLM Model for Human-like Reasoning
Anshul Nayak, Shahil Shaik, Yue Wang
Traditional neural network models for intent inference rely heavily on observable states and struggle to generalize across diverse tasks and dynamic environments. Recent advances i…
Distribution-Centric Policy Optimization Dominates Exploration-Exploitation Trade-off
Zhaochun Li, Chen Wang, Jionghao Bai +4
The exploration-exploitation (EE) trade-off is a central challenge in reinforcement learning (RL) for large language models (LLMs). With Group Relative Policy Optimization (GRPO),…
SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training
Chen Wang, Zhaochun Li, Jionghao Bai +4
Reinforcement learning (RL) is a key paradigm for post-training large language models (LLMs), but the widely used Group Relative Policy Optimization (GRPO) often suffers from entro…
CAMF: Collaborative Adversarial Multi-agent Framework for Machine Generated Text Detection
Yue Wang, Liesheng Wei, Yuxiang Wang
Detecting machine-generated text (MGT) from contemporary Large Language Models (LLMs) is increasingly crucial amid risks like disinformation and threats to academic integrity. Exis…
No Free Lunch: Rethinking Internal Feedback for LLM Reasoning
Yanzhi Zhang, Zhaoxi Zhang, Haoxiang Guan +6
Reinforcement learning has emerged as a powerful paradigm for post-training large language models (LLMs) to improve reasoning. Approaches like Reinforcement Learning from Human Fee…
EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
Chen Wang, Lai Wei, Yanzhi Zhang +5
Recent advances in reinforcement learning (RL) have significantly enhanced the reasoning capabilities of large language models (LLMs). Group Relative Policy Optimization (GRPO), a…
First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training
Lai Wei, Yuting Li, Chen Wang +4
Improving Multi-modal Large Language Models (MLLMs) in the post-training stage typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL), which require expens…
Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start
Lai Wei, Yuting Li, Kaipeng Zheng +5
Recent advancements in large language models (LLMs) have demonstrated impressive chain-of-thought reasoning capabilities, with reinforcement learning (RL) playing a crucial role in…