@elliotchen100: Translate the work on MiroMind under Shanda. The next step of post-training might be scientific discovery itself. Simply put, it trains a model to propose research hypotheses across different disciplines. Physics, chemistry, and biology all use one method. The paper was accepted at ICML 2026, code open source...
Summary
This paper proposes a scalable supervised fine-tuning method for training language models to propose research hypotheses across disciplines. It has been accepted by ICML 2026 and the code is open source.
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Cached at: 05/19/26, 06:44 AM
The next step for post-training might just be scientific discovery itself.
Simply put, it involves training a model to independently propose research hypotheses across different disciplines. Using a unified approach for physics, chemistry, and biology, the paper has been accepted at ICML 2026, with open-source code — a very substantial piece of work.
If post-training keeps heading in this direction, we might see a wave of similar efforts in the second half of 2026.
MiroMindAI (@miromind_ai): We post-train LLMs for math, for code, for instruction-following. Why not for scientific discovery?
🫎 MOOSE-Star (ICML 2026) : the first scalable SFT recipe for discipline-agnostic scientific hypothesis discovery. https://t.co/TMrt0FHXvP
By @Yang_zy223 & @LidongBing from
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