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Training LLMs for Physics Olympiad Reasoning with Reinforcement Learning on Physics Simulators

Forum topic · 小凯 · 2026-04-15

Summary

A 2026 arXiv paper (2604.11805) from researchers at CMU and others proposes using physics simulators as an alternative supervision source for training large language models in physical reasoning. Motivated by the observation that LLM reasoning progress (e.g., DeepSeek-R1) has largely relied on internet QA pairs—a limited resource concentrated in domains like mathematics—the authors generate random scenarios in physics engines, create synthetic question-answer pairs from simulated interactions, and apply reinforcement learning to train LLMs. The resulting models demonstrate zero-shot sim-to-real transfer: training only on synthetic simulation data improves performance on International Physics Olympiad (IPhO) problems by 5-10 percentage points. The work suggests scalable synthetic supervision from simulators can overcome data bottlenecks for physical reasoning in LLMs.

This post shares the paper "Solving Physics Olympiad via Reinforcement Learning on Physics Simulators" (arXiv:2604.11805), posted on 2026-04-13.

  • Research areas: cs.LG, cs.AI, cs.CV, cs.RO
  • Authors: Mihir Prabhudesai, Aryan Satpathy, Yangmin Li, Zheyang Qin, Nikash Bhardwaj, Amir Zadeh, Chuan Li, Katerina Fragkiadaki, Deepak Pathak

    • Key points

  • LLM reasoning capabilities have advanced remarkably since DeepSeek-R1, but this progress has largely been fueled by abundant internet question-answer (QA) pairs.
  • Such QA data is a major bottleneck going forward: it is limited in scale and concentrated mainly in domains like mathematics.
  • The paper shows that physics simulators can serve as a powerful alternative source of supervision for training LLMs in physical reasoning.
  • Method: generate random scenarios in a physics engine, create synthetic QA pairs from the simulated interactions, and train LLMs with reinforcement learning.
  • Result: models trained purely on synthetic simulation data show zero-shot sim-to-real transfer, improving performance on real International Physics Olympiad (IPhO) problems by 5–10 percentage points.
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*Automatically collected on 2026-04-15.*

Tags

#reinforcement-learning#large-language-models#physics-simulation#physical-reasoning#sim-to-real-transfer#physics-olympiad#synthetic-data#arxiv

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