mlsanityData to 7 Oct 2026

MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

arXiv 2512.21231 ↗First posted 24 Dec 2025cs.LG cond-mat.mtrl-scicited by 2

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 57 of its 90 references that are in the map. First posted on arXiv 24 Dec 2025.

Builds on

57 of 90 references.

Closest ancestors · 3

Same direction, newest first

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  2. 2025Training a Scientific Reasoning Model for Chemistry4 Jun
  3. 2025Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation11 Mar

Other references · 54

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesiscited by 4
  2. 2026BOReFT: Manifold Steering of Language Models for Black-box Optimizationcited by 0

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