Official Implementation of Video-T1: Test-Time Scaling for Video Generation

Official codebase for "Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling".

Scale your LLM-as-a-judge.

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Official codebase for "GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning".

🔥[ICML'25] Official repository for the paper "Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search"

A comrephensive collection of learning from rewards in the post-training and test-time scaling of LLMs, with a focus on both reward models and learning strategies across training, inference, and post-inference stages.

"what, how, where, and how well? a survey on test-time scaling in large language models" repository

m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning in Large Language Models

ACL'2025: SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs. and preprint: SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning

The official PyTorch implementation for the Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence

Complexity-Aware inference time Reasoning for Text-to-SQL via exploration and compute budget allocation

In which language do these models reason when solving problems presented in different languages? Our findings reveal that, despite multilingual training, LRMs tend to default to reasoning in high-resource languages (e.g., English) at test time

Official implementation for the paper "First Finish Search: Efficient Test-Time Scaling in Large Language Models"

Guided by Gut offers a streamlined approach to Test-Time Scaling using advanced reinforcement learning techniques. Explore the repository for practical implementations and insights into confidence-based fine-tuning and self-guided tree search. 🐙🌟