8 papers
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Xinyu Tang, Qianggang Cao, Yurou Liu +13
The paper introduces a training pipeline that scales zero‑reinforcement‑learning to a trillion‑parameter language model, revealing emergent chain‑of‑thought reasoning abilities and…
ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution
Zican Dong, Peiyu Liu, Junyi Li +4
Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces. However, as the sequence length grows, the key-value (KV) cache…
Improving Vision-language Models with Perception-centric Process Reward Models
Yingqian Min, Kun Zhou, Yifan Li +6
Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-language models (VLMs). However, it…
How Efficient Are Diffusion Language Models? A Critical Examination of Efficiency Evaluation Practices
Han Peng, Peiyu Liu, Zican Dong +5
Diffusion language models (DLMs) have emerged as a promising alternative to the long-dominant autoregressive (AR) paradigm, offering a parallelable decoding process that could yiel…
Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation
Canxiang Yan, Chunxiang Jin, Dawei Huang +22
Existing speech models suffer from competing requirements on token representations by understanding and generation tasks. This discrepancy in representation prevents speech languag…
MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
Jisheng Dang, Huilin Song, Junbin Xiao +6
Grounded Video Question Answering (Grounded VideoQA) requires aligning textual answers with explicit visual evidence. However, modern multimodal models often rely on linguistic pri…