TTT + RL: Stanford/Nvidia Beat AlphaEvolve
Source: https://x.com (tweet) Author: unknown Date: 2025-2026
Summary
Stanford/Nvidia paper combining test-time training (TTT) with RL achieves remarkable results: beats DeepMind AlphaEvolve, discovers a new upper bound for Erdős’s minimum overlap problem. Open-source model. Thread calling the results “crazy.”
Key Claims
- TTT + RL combination on open-source model beats DeepMind’s AlphaEvolve
- New mathematical discovery: upper bound for Erdős’s minimum overlap problem
- Open-source approach — democratizing frontier research capability
- Combines two scaling axes (test-time compute + RL) into one training paradigm
Connection to Other Sources
Connects to Test-Time Compute and Autonomous Research. If agents can now beat specialized AI systems (AlphaEvolve) in math discovery, this has implications for automated research.
Concepts
- Test-Time Compute — TTT as inference-time adaptation
- RL Infrastructure — RL + TTT combination
- Autonomous Research — discovering mathematical results autonomously