5 papers · 1 filter
SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing
Ruikang Zhao, Zhenting Wang, Han Gao +1
Reinforcement learning for diffusion large language models (dLLMs) has largely moved to trajectory-aware methods. The current state of the art, TraceRL, holds that random masking i…
S2D2: Fast Decoding for Diffusion LLMs via Training-Free Self-Speculation
Ligong Han, Hao Wang, Han Gao +2
Block-diffusion language models offer a promising path toward faster-than-autoregressive generation by combining block-wise autoregressive decoding with within-block parallel denoi…
Training Report of TeleChat3-MoE
Xinzhang Liu, Chao Wang, Zhihao Yang +51
TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one t…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
Ruikang Liu, Haoli Bai, Haokun Lin +6
Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they comp…