--- title: '[2211.14275] Solving math word problems with process- and outcome-based feedback' id: 221114275-solving-math-word-problems-with-process-and-outcome-based-feedback tags: - socratic-mcts-swe-worldmodel-8f6dea created: '2026-06-09T04:23:24.366439Z' updated: '2026-06-09T04:23:56.531901Z' source: https://arxiv.org/abs/2211.14275 source_domain: arxiv.org fetched_at: '2026-06-09T04:23:24.269109Z' fetch_provider: builtin status: draft type: note tier: institutional content_type: paper deprecated: false summary: 'DeepMind (Uesato et al. 2022): the original head-to-head of process- vs outcome-based feedback; final-answer error parity but process feedback drastically cuts reasoning/trace errors — motivates rewarding the path, not just the result.' --- [2211.14275] Solving math word problems with process- and outcome-based feedback Computer Science > Machine Learning arXiv:2211.14275 (cs) [Submitted on 25 Nov 2022] Title: Solving math word problems with process- and outcome-based feedback Authors: Jonathan Uesato , Nate Kushman , Ramana Kumar , Francis Song , Noah Siegel , Lisa Wang , Antonia Creswell , Geoffrey Irving , Irina Higgins View a PDF of the paper titled Solving math word problems with process- and outcome-based feedback, by Jonathan Uesato and 8 other authors View PDF Abstract: Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervise the reasoning process itself? Differences between these approaches might naturally be expected not just in final-answer errors but also in reasoning errors, which can be difficult to detect and are problematic in many real-world domains such as education. We run the first comprehensive comparison between process- and outcome-based approaches trained on a natural language task, GSM8K. We find that pure outcome-based supervision produces similar final-answer error rates with less label supervision. However, for correct reasoning steps we find it necessary to use process-based supervision or supervision from learned reward models that emulate process-based feedback. In total, we improve the previous best results from 16.8% $\to$ 12.7% final-answer error and 14.0% $\to$ 3.4% reasoning error among final-answer-correct solutions. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2211.14275 [cs.LG] (or arXiv:2211.14275v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2211.14275 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jonathan Uesato [ view email ] [v1] Fri, 25 Nov 2022 18:19:44 UTC (306 KB) Full-text links: Access Paper: View a PDF of the paper titled Solving math word problems with process- and outcome-based feedback, by Jonathan Uesato and 8 other authors View PDF TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2022-11 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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