Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials

• URL: http://arxiv.org/abs/2606.21830v1
• Date: Sat, 20 Jun 2026 01:46:41 GMT
• Title: Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials
• Abstract summary: Mat-Pref is a benchmark of 10,837 ionic-substitution questions across 11 inorganic structure families.<n>Four zero-shot frontier models remain in the 33-54% range on every split, confirming that scale alone does not resolve the compositional chemical reasoning this task demands.
• Score: 2.102846336724103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning from verifiable rewards (RLVR) has driven rapid progress in mathematical and code reasoning, but when extended to science, existing benchmarks do not decompose what generalizes: do gains reflect structural transfer, property transfer, or memorization? We introduce Mat-Pref, a benchmark of 10,837 ionic-substitution questions across 11 inorganic structure families, grounded in density functional theory calculations from the Materials Project, with three evaluation splits that isolate in-distribution performance, generalization to entirely held-out structure families, and cross-property transfer: applying band-gap reasoning to hosts seen during training only through formation-energy supervision. Four zero-shot frontier models (70-671B parameters) remain in the 33-54% range on every split, confirming that scale alone does not resolve the compositional chemical reasoning this task demands. A two-stage pipeline of supervised fine-tuning followed by Group Relative Policy Optimization (GRPO) lifts Qwen3-8B to 65.2% in-distribution and 71.6% on held-out families, exceeding zero-shot Qwen3-235B by over 20 percentage points on both structural-generalization splits. Self-consistency sampling shows that the SFT policy can already produce correct answers but cannot reliably surface them as the modal response; GRPO reshapes the distribution so that correct answers become modal rather than merely reachable, and this sharper commitment is visible mechanistically: logit lens analysis reveals a ${\sim}$20pp advantage in answer crystallization at the critical decision layer. We formalize this observation as a distractor-permutation consistency metric under which GRPO narrows the gap between lenient scoring (at least one permutation correct) and strict scoring (all permutations correct) from 24.0 to 14.3 percentage points.

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