8 papers · 1 filter
Variational Reasoning for Language Models
Xiangxin Zhou, Zichen Liu, Haonan Wang +5
We introduce a variational reasoning framework for language models that treats thinking traces as latent variables and optimizes them through variational inference. Starting from t…
Language Models Can Learn from Verbal Feedback Without Scalar Rewards
Renjie Luo, Zichen Liu, Xiangyan Liu +5
LLMs are often trained with RL from human or AI feedback, yet such methods typically compress nuanced feedback into scalar rewards, discarding much of their richness and inducing s…
LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification
Penghui Yang, Cunxiao Du, Fengzhuo Zhang +4
As Large Language Models (LLMs) can now process extremely long contexts, efficient inference over these extended inputs has become increasingly important, especially for emerging a…
Improving Your Model Ranking on Chatbot Arena by Vote Rigging
Rui Min, Tianyu Pang, Chao Du +3
Chatbot Arena is a popular platform for evaluating LLMs by pairwise battles, where users vote for their preferred response from two randomly sampled anonymous models. While Chatbot…
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training
Haonan Wang, Qian Liu, Chao Du +4
Extending context window sizes allows large language models (LLMs) to process longer sequences and handle more complex tasks. Rotary Positional Embedding (RoPE) has become the de f…
When Attention Sink Emerges in Language Models: An Empirical View
Xiangming Gu, Tianyu Pang, Chao Du +5
Language Models (LMs) assign significant attention to the first token, even if it is not semantically important, which is known as attention sink. This phenomenon has been widely a…