🚀 MassGen: An Open-source Multi-Agent Scaling System Inspired by Grok Heavy and Gemini Deep Think. Join the discord channel: https://discord.com/invite/VVrT2rQaz5
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Updated
Aug 12, 2025 - Python
🚀 MassGen: An Open-source Multi-Agent Scaling System Inspired by Grok Heavy and Gemini Deep Think. Join the discord channel: https://discord.com/invite/VVrT2rQaz5
[ICCV 2025] Video-T1: Test-Time Scaling for Video Generation
Inference-time scaling for LLMs-as-a-judge.
Official codebase for "Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling".
Repo of paper "Free Process Rewards without Process Labels"
🔥[ICML'25] Official repository for the paper "Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search"
Official codebase for "GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning".
Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning
"what, how, where, and how well? a survey on test-time scaling in large language models" repository
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.
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
Awesome-Parallel-Reasoning: Unlocking the reasoning potential of LLMs. Papers, Code, Resources & Upcoming Survey.
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 of the paper "First Finish Search: Efficient Test-Time Scaling in Large Language Models"
Revolutionary Self-Evolving Language Model - 100% self-contained AI trained on 40M Wikipedia articles
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