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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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“As one of the earliest policy gradient algorithms, REINFORCE (Williams, 1992) provides the foundational theory for training stochastic policies.”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Williams, 1992
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Metadata
- Authors
- Williams, R. J.
- Venue
- Machine Learning
- Published
- 1992-05
- Volume
- 8
- Issue
- 3-4
- Pages
- 229-256
- ISSN
- 0885-6125
- 1573-0565
- Cited by
- 3,416
- References
- 4
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
