118 citations · 471 across the 34 of their papers we have counts for
40 papers
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.…
Can Computation from Earlier Problems Help LLMs Solve New Ones?
Jipei He, Wenhui Tan, Xiaoyi Yu +4
Large language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answer this question, we first c…
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…
ABRA: Teleporting Fine-Tuned Knowledge Across Domains for Open-Vocabulary Object Detection
Mattia Bernardi, Chiara Cappellino, Matteo Mosconi +3
Although recent Open-Vocabulary Object Detection architectures, such as Grounding DINO, demonstrate strong zero-shot capabilities, their performance degrades significantly under do…
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…