MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models
Andres M Bran, Tong Xie, Shai Pranesh, Jeffrey Meng, Xuan Vu Nguyen, Jeremy Goumaz, David Ming Segura, Ruizhi Xu, Dongzhan Zhou, Wenjie Zhang, Bram Hoex, Philippe Schwaller
TL;DR
This paper introduces MiST, a set of mid-stage training techniques that enhance chemical reasoning in language models by satisfying key prerequisites, leading to significant improvements in accuracy and interpretability across chemical tasks.
Contribution
The paper proposes MiST, a novel mid-stage training approach that improves chemical reasoning in language models by increasing latent chemical knowledge and symbolic competence.
Findings
Latent solvability is crucial for reinforcement learning success in chemical reasoning.
MiST techniques significantly improve model accuracy on chemical tasks.
Enhanced models produce interpretable reasoning traces.
Abstract
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…
Peer Reviews
Decision·ICLR 2026 Conference Desk Rejected Submission
1. Robust Data Construction from Diverse Sources. The model benefits from a high-quality 2.9B token corpus for continued pre-training (MiST), drawing extensively from Diverse sources such as ChemRxiv + S2ORC and PubChem synthetic data. Additional datasets including both instruction-following dataset and reasoning traces from DeepSeek-R1 are also collected for enhancing the chemical reasoning abilities. 2. Effective Multi-Stage Training Pipeline. The training incorporates continued pre-train
1. No experiments to show improvement in terms of SMILES generation It's unclear whether after the extensive training with data including SMILES strings, LLM can successfully generate accurate SMILES strings while retaining good reasoning abilities now. Although the paper includes CCS and SCS scores for comparison, it is not explicit enough about the improvement in the ability to generate reasonable and correct SMILES strings. 2. No experiments to show improvement in terms of reasoning abilit
The idea of constructing criteria to determine whether a model has the potential for reasoning training is interesting and creative. Although not adequate, their experiments provide preliminary evidence that the criteria they proposed are effective to some extent.
1. The paper is ill-organized, to the point of significantly hindering readability. * Their citations have severe problems. When I try to look up one of their citation, 'ChemLLM', neither the title, the authors, nor the ArXiv ID matched what was listed in their paper. I then searched using the given ArXiv ID and found that the title and authors were completely different, and clearly inconsistent with the intended citation context. I was therefore unable to locate the paper they referenced, w
**(S1 - relevance, novelty) - Relevance of the raised research question.** I share the authors’ view of RL as an amplifier for knowledge that is already present but latent within the base model. It is almost a general assumption that RL (given sufficient compute budget) will succeed if the base model is sufficiently capable. However, it remains unclear what exactly constitutes a “good base model”. This is precisely the research question addressed here: “What pretraining and prerequisites must an
... however, the second part of the manuscript (from Section 4 onward) feels like a completely separate work. The storytelling flow breaks at several points — in fact, the raised research questions are never answered, and the experiment is unsuitable. Several text passages lose their focus; for example, Section 4 describes in great technical detail how to derive flattened text (which is not particularly interesting and could be moved to the appendix) instead of providing details about the traini
- **Novel diagnostic approach:** The SCS/CCS metrics attempt to quantify a priori readiness for RL training rather than just measuring end performance. This predictive framing—can we assess whether RL will work before expensive training?—is valuable and timely. - **Systematic investigation of prerequisites:** Unlike prior work that simply applies RL to chemistry, this paper explicitly decomposes what's needed (symbolic competence + domain knowledge) and attempts to measure each component separat
- **Inadequate statistical validation:** No error bars, confidence intervals, or significance tests are reported. Single-run experiments on one backbone (Qwen-2.5-3B) prevent generalization claims and drastically limit the insight of this work. - **Missing baselines:** Only NatureLM is compared (appendix only); recent models like Intern-S1-mini [1] and ether0 [2] are ignored. - **Incomplete ablations:** No compute-matched SFT+RL baseline without MiST across all tasks—essential to isolate MiST's
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Taxonomy
TopicsMachine Learning in Materials Science · Topic Modeling · Multimodal Machine Learning Applications