8 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
On-policy Distillation with Verifiable Reward
Wenze Lin, Jiale Zhao, Xitai Jiang +5
Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training large language models. However, RL…
Action with Visual Primitives
Weilong Guo, Yuchen Wang, Renping Zhou +5
Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation. A common design in current architectures maps language instructions an…
From Reasoning Chains to Verifiable Subproblems: Curriculum Reinforcement Learning Enables Credit Assignment for LLM Reasoning
Xitai Jiang, Zihan Tang, Wenze Lin +3
Reinforcement learning from verifiable rewards (RLVR) has shown strong promise for LLM reasoning, but outcome-based RLVR remains inefficient on hard problems because correct final-…
Boosting LLM Reasoning via Human-Inspired Reward Shaping
Wenze Lin, Zhen Yang, Xitai Jiang +2
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for enhancing reasoning in Large Language Models (LLMs). However, existing reward formulat…
SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning
Yong Xien Chng, Tao Hu, Wenwen Tong +10
While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated t…