Test-Time Training: Models That Keep Learning While You Use Them
TheTuringPost · x · 2026-09-13
Turing Post publishes a guide to Test-Time Training—how models can keep learning during inference and why it may be key to overcoming agent and world model limitations. Using OpenAI's Navier–Stokes experiment as a case study, it compares GPT-6 Astra with OpenAI's next-gen internal model on open math problems: the new model starts higher, and added test-time compute pushes both further, with gains coming partly from stronger training and partly from more inference-stage compute.
More from Research
- 2-Step Distill LoRA for Krea 2 Turbo Cuts Denoising Time 3.8x — TimeTruth2490 · 2026-09-13
- davidad: R1-Zero published the recipe for 'data criticality' — literally the singularity — davidad · 2026-09-13
- terms.txt paper proposes machine-readable access terms and pricing for AI agents — dair_ai · 2026-09-13
- Decagon on GEPA-GAN: Simulated Users That Are Too Cooperative Are Skewing Agent Evals — kastnerkyle · 2026-09-13
- AVERI Paper on Frontier AI Auditing Backs Dario's Embedded Evaluator Commitment — Miles_Brundage · 2026-09-13
- AI math proofs won't kill understanding: post hoc exploration keeps mathematicians central — njyx · 2026-09-13