6 papers · 1 filter
Frustratingly Easy Majority Voting Using Length Conditioning
Fiorenzo Parascandolo, Enver Sangineto, Xiaoyi Yu +4
Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional inference-time computation.…
Informed Masking: Structure-Aware Perturbation for Reinforcement Learning in Diffusion Large Language Models
Xiaoyi Yu, Enver Sangineto, Pei Fu +6
Diffusion Large Language Models (dLLMs) have emerged as an efficient alternative to autoregressive models, yet aligning them via Reinforcement Learning (RL) requires likelihood sur…
Pair-In, Pair-Out: Latent Multi-Token Prediction for Efficient LLMs
Wenhui Tan, Minghao Li, Xiaoqian Ma +5
Long chain-of-thought reasoning has made autoregressive decoding the dominant inference cost of modern large language models. Existing methods target either the input side (latent…
BFS-PO: Best-First Search for Large Reasoning Models
Fiorenzo Parascandolo, Wenhui Tan, Enver Sangineto +2
Large Reasoning Models (LRMs) such as OpenAI o1 and DeepSeek-R1 have shown excellent performance in reasoning tasks using long reasoning chains. However, this has also led to a sig…
Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Models
Wenhui Tan, Fiorenzo Parascandolo, Enver Sangineto +6
Large Reasoning Models (LRMs) have recently achieved strong mathematical and code reasoning performance through Reinforcement Learning (RL) post-training. However, we show that mod…
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains
Wenhui Tan, Jiaze Li, Jianzhong Ju +3
Large Language Models (LLMs) achieve superior performance through Chain-of-Thought (CoT) reasoning, but these token-level reasoning chains are computationally expensive and ineffic…