3 citations · 3 across the 18 of their papers we have counts for
11 papers · 1 filter
Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization
Hao Bai, Jiarui Yao, Tong Zhang
Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response reuse. Token-wise importanc…
Game-Creation-Agents: Autonomous Game Production
Tencent Multimodal RL Team, Xiangxin Zhou, Jiaqi Tang +15
Game-Creation-Agents (GCA) turns a natural-language request into a runnable Phaser 4 browser game. The pipeline first builds a multimodal game design document that couples mechanic…
LMBuild: Evaluating LLM Agents for Generating Buildable and Functional Structures
Jiateng Liu, Rushi Wang, Cheng Qian +9
LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, produ…
MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation
Bingxuan Li, Siqi Song, Yizhuo Wu +3
Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on spec…
SceneActBench: Can Agents Act on the 3D Scenes They See?
Yifei Zhao, Xiangxin Zhou, Wenhao Yang +11
Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operat…
Predictive Divergence Masks for LLM RL
Xiangxin Zhou, Jiarui Yao, Penghui Qi +4
Reinforcement learning for large language models (LLMs) typically relies on trust-region masks to stabilize off-policy updates. The dominant PPO-style approach uses the sampled-tok…
GRAIN: Group Aggregation via Min-Norm Objective
Nghia Bui, Jiarui Yao, Lijing Wang
Learning instability is a long-standing problem across machine learning, but it is especially acute in the overparameterized regime that defines modern deep learning: large models…
Rethinking the Divergence Regularization in LLM RL
Jiarui Yao, Xiangxin Zhou, Penghui Qi +3
Reinforcement learning (RL) has become a key component of post-training large language models (LLMs). In practice, LLM RL is often off-policy because of training-inference mismatch…
AgentSPEX: An Agent SPecification and EXecution Language
Pengcheng Wang, Jerry Huang, Jiarui Yao +7
Language-model agent systems commonly rely on reactive prompting, in which a single instruction guides the model through an open-ended sequence of reasoning and tool-use steps, lea…
Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
Haitao Jiang, Wenbo Zhang, Jiarui Yao +3
Pre-trained Large Language Model (LLM) exhibits broad capabilities, yet, for specific tasks or domains their attainment of higher accuracy and more reliable reasoning generally dep…
Future-KL Regularized GRPO: Process-Level Credit Assignment from -Divergence Regularization
Jiarui Yao, Ruida Wang, Hao Bai +1
Group Relative Policy Optimization (GRPO) is widely used for critic-free Large Language Model (LLM) post-training, but its KL regularization is usually implemented as a local loss-…