Papers › Learning Multi-Step Reasoning by Solving Arithmetic Tasks
Learning Multi-Step Reasoning by Solving Arithmetic Tasks
Tianduo Wang, Wei Lu
Mathematical reasoning is regarded as a necessary ability for Language Models (LMs). Recent works demonstrate large LMs' impressive performance in solving math problems. The success is attributed to their Chain-of-Thought (CoT) reasoning abilities, i.e., the ability to decompose complex questions into step-by-step reasoning chains, but such ability seems only to emerge from models with abundant parameters. This work investigates how to incorporate relatively small LMs with the capabilities of multi-step reasoning. We propose to inject such abilities by continually pre-training LMs on a synthetic dataset MsAT which is composed of Multi-step Arithmetic Tasks. Our experiments on four math word problem datasets show the effectiveness of the proposed method in enhancing LMs' math reasoning abilities.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Math Word Problem Solving | MAWPS | MsAT-DeductReasoner | Accuracy (%) | 94.3 | #2 of 25 | Archive leaderboard | report |
| Math Word Problem Solving | SVAMP | MsAT-DeductReasoner | Execution Accuracy | 48.9 | #18 of 26 | Archive leaderboard | report |
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