Physics-Reasoning

TL;DR for operators A simulator can generate effectively unlimited trajectories. That does not automatically make those trajectories useful training data for a reasoning model. Sim2Reason turns simulated mechanics into questions with answers that can be checked automatically, then uses those questions for reinforcement-learning post-training. The reported transfer is substantial enough to matter operationally: on Qwen2.5-32B, International Physics Olympiad mechanics accuracy rose from 19.8% to 25.2%. By contrast, supervised fine-tuning on 200,000 teacher-generated trajectories lowered the same score to 15.9%. ...

TL;DR for operators A familiar enterprise AI failure looks like this: the model gives a confident answer, the formatting is exquisite, the explanation sounds like a gifted teaching assistant, and one equation quietly takes the project into a ditch. Physics is an unusually good place to study that failure because being clear is not enough. The system must interpret the situation, select the right principle, keep the units straight, calculate correctly, and not hallucinate a helpful-but-illegal assumption because the prompt looked lonely. ...