5 citations · 11 across the 18 of their papers we have counts for
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Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Giorgio Giannone, Guangxuan Xu, Nikhil Shivakumar Nayak +4
Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a strong ITS method for complex math…
Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement
Meihua Dang, Jiaqi Han, Minkai Xu +3
Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training. However, inferenc…
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han +8
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. E…
SQuat: Subspace-orthogonal KV Cache Quantization
Hao Wang, Ligong Han, Kai Xu +1
The key-value (KV) cache accelerates LLMs decoding by storing KV tensors from previously generated tokens. It reduces redundant computation at the cost of increased memory usage. T…
Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
Isha Puri, Shivchander Sudalairaj, Guangxuan Xu +2
Large language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such app…