10 papers
: Faster Test-Time Scaling through Speculative Drafts
Mert Cemri, Nived Rajaraman, Rishabh Tiwari +6
Scaling test-time compute has driven the recent advances in the reasoning capabilities of large language models (LLMs), typically by allocating additional computation for more thor…
InfAlign: Inference-aware language model alignment
Ananth Balashankar, Ziteng Sun, Jonathan Berant +9
Language model alignment is a critical step in training modern generative language models. Alignment targets to improve win rate of a sample from the aligned model against the base…
On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows
Souradip Chakraborty, Mohammadreza Pourreza, Ruoxi Sun +8
Agentic AI workflows (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low. A promising solution is inference-t…
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
Soumya Suvra Ghosal, Souradip Chakraborty, Vaibhav Singh +7
With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that des…
Theoretical guarantees on the best-of-n alignment policy
Ahmad Beirami, Alekh Agarwal, Jonathan Berant +4
A simple and effective method for the inference-time alignment and scaling test-time compute of generative models is best-of- sampling, where samples are drawn from a refere…
CoDe: Blockwise Control for Denoising Diffusion Models
Anuj Singh, Sayak Mukherjee, Ahmad Beirami +1
Aligning diffusion models to downstream tasks often requires finetuning new models or gradient-based guidance at inference time to enable sampling from the reward-tilted posterior.…