research/notes/221114275-solving-math-word-problems-with-process-and-outcome-based-feedback.md

---
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
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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)
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