1 citations · 1 across the 9 of their papers we have counts for
12 papers
Efficient Training of Diffusion Mixture-of-Experts Models: A Practical Recipe
Yahui Liu, Yang Yue, Jingyuan Zhang +5
Recent efforts on Diffusion Mixture-of-Experts (MoE) models have primarily focused on developing more sophisticated routing mechanisms. However, we observe that the underlying arch…
Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
Qi Wang, Hongzhi Zhang, Jia Fu +12
Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In thi…
Accelerating Diffusion LLM Inference via Local Determinism Propagation
Fanheng Kong, Jingyuan Zhang, Yahui Liu +4
Diffusion large language models (dLLMs) represent a significant advancement in text generation, offering parallel token decoding capabilities. However, existing open-source impleme…
Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Jia Fu, Xinyu Yang, Hongzhi Zhang +5
Precise, correct feedback is crucial for effectively training large language models (LLMs) in code reinforcement learning. However, synthesizing high-quality test cases remains a p…
AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learning
Shihao Yuan, Yahui Liu, Yang Yue +5
Inspired by the success of reinforcement learning (RL) in refining large language models (LLMs), we propose AR-GRPO, an approach to integrate online RL training into autoregressive…
Leanabell-Prover-V2: Verifier-integrated Reasoning for Formal Theorem Proving via Reinforcement Learning
Xingguang Ji, Yahui Liu, Qi Wang +7
We introduce our Leanabell-Prover-V2, a 7B large language models (LLMs) that can produce formal theorem proofs in Lean 4, with verifier-integrated Long Chain-of-Thoughts (CoT). Fol…