Skip to content

NotablePeer-reviewedVideo

DeepSeek-R1: reasoning learned through pure reinforcement learning

An open model develops self-reflection and verification with no human reasoning examples.

ByDaya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu and 2 others

DeepSeek (China) · University of Science and Technology of China · Peking University

32nd of 39 in AI & Computing on Frontier, by Frontier Score.

What it is

DeepSeek-R1 shows that a large language model can be taught to reason through pure reinforcement learning on verifiable tasks, with no human-written reasoning traces to imitate. Under this training, behaviors like self-reflection, verification, and dynamic strategy adaptation emerge on their own. The resulting model outperforms counterparts trained by conventional supervised learning on human demonstrations across mathematics, coding competitions, and STEM problems.

Fig. 1 of the paper: Accuracy and output length of DeepSeek-R1-Zero throughout the training process.
Fig. 1 from the paperAccuracy and output length of DeepSeek-R1-Zero throughout the training process.Daya Guo et al., Nature (2025), under CC BY 4.0, shown unaltered; image from PubMed Central.

Why it matters

This is the first peer-reviewed demonstration (a Nature cover) that advanced reasoning can be incentivized by RL alone, removing the dependence on costly human-annotated reasoning data. The emergent reasoning patterns also transfer, guiding and improving smaller models.

Every metric behind this entry is listed, with its source, under Sources and data below.

Filed underreinforcement learning, reasoning, open models, LLM, DeepSeek

Watch

A short explainer of this result.

Scored 2026-11-13How scoring works

The Frontier Score, in full

How this entry's score is built, term by term. Open any pillar to see each sub-metric's raw value, how it maps to 0 to 100, and a link to its source (OpenAlex for most sub-metrics that combine several sources, NIH iCite where its ratio is part of field-normalized impact).
Evidence 97Impact 100Novelty 43
The three pillars as one shape: a result strong on every axis fills the triangle.
How the Frontier Score is calculated for this breakthrough
TermScoreWeightPoints
EvidenceHow established and verifiable the result is.97× 0.2524.3
ImpactHow much the result matters, judged field-relative and by current momentum.100× 0.2524.9
NoveltyHow genuinely new the result is, and how fast-moving.43× 0.5021.6
Weighted pillars70.8
Frontier recency14 months since publication× 0.65
IntegrityNot retracted× 1
Frontier Score46
Confidence: Settled. Established evidence: about 1471 citations across 5 independent indices. Corroborated beyond citations (cited on Wikipedia).Established evidence: about 1471 citations across 5 independent indices. Corroborated beyond citations (cited on Wikipedia).

Every sub-metric, traced to its source

Evidence97
Evidence sub-metrics
Sub-metricRaw value0 to 100WeightPointsSource
Peer review & venueWhether the work has cleared peer review, and how selective its venue is. Peer review is necessary but not sufficient: before citations accrue, a fresh result in one of the five general flagships (Nature, Science, Cell, NEJM, the Lancet) is a stronger evidence signal than one in a selective specialist or society journal, which in turn outranks one in a legitimate but very high-volume mega-journal, and any of them outranks a preprint that has not been reviewed at all.Maps to 0 to 100: Peer-reviewed article graded by venue tier: flagship journal (Nature, Science, Cell, NEJM, the Lancet) 100; selective journal (every other journal the index admits: the specialist journals of those families, JAMA, PNAS, Physical Review Letters and X) 90; high-volume mega-journal (Nature Communications, Science Advances) 78; venue not recorded 88. Other records: review 90, book 70, dataset 60, preprint 45, an article that has not been peer reviewed 45, other 55.Peer-reviewed, flagship journal1000.3030.0OpenAlex type / primary_location / source
CorroborationIndependent corroboration that the result is on the record and reaching beyond a single source, read as the BREADTH of independent records rather than the size of the citation pile. Counts how many of the five independent citation indices (OpenAlex, Crossref, Semantic Scholar, OpenCitations, Europe PMC) report a citation count for the work at all, zero included, plus orthogonal, non-citation lines of corroboration: citation in Wikipedia, registered clinical trials or clinical citations, released datasets or software or a repository copy, public funding on record, and technical-community discussion. Citation MAGNITUDE is deliberately judged under Impact, not here, so Evidence and Impact measure genuinely different things instead of both rewarding the same citation pile twice.Maps to 0 to 100: Starts at 28 and rises by 9 for each of the five independent citation indices that returns a citation count for the work (zero counts; OpenCitations returns zero even for a DOI it does not hold) and by 6 for each orthogonal non-citation line (Wikipedia, clinical trials or clinical citations, open datasets, software or a repository copy, public funding, community discussion), up to the top of the 0-100 scale.5 of 5 citation indices report a count, 3 independent non-citation lines910.3027.3OpenAlex, Crossref, Semantic Scholar, OpenCitations, Europe PMC, DataCite, NIH RePORTER, Wikipedia, Hacker News count of indices that report a count for the work + orthogonal reach
VerifiabilityHow openly the result can be read, reused, and reproduced. Rewards open access, a Creative Commons BY or CC0 license, a green repository copy anyone can archive, openly minable full text, and released datasets or software a reader can actually run. Cross-checked across OpenAlex, Unpaywall, Europe PMC, and DataCite.Maps to 0 to 100: Closed access 45; open access 88; any Creative Commons BY license (including the NC, ND and SA variants) or CC0 100. A repository copy raises the floor to 85, openly minable full text to 90, and released data or software to 92.Open access (hybrid), CC-BY, repository copy1000.2020.0OpenAlex, Unpaywall, Europe PMC, DataCite is_oa / license / has_repository_copy / open datasets
IntegrityRetraction and post-publication correction status. A retracted result is not evidence of anything and collapses the whole Frontier Score; a correction or expression of concern is a softer flag. A correction the publisher labels as its own error (a Publisher Correction) is not a flag on the work. Read across OpenAlex and Crossref (both update-to and updated-by notices).Maps to 0 to 100: Clean 100 (also when the only notice is a Publisher Correction); a correction or expression of concern 75; retracted 0. Flagged if OpenAlex OR Crossref records the notice.No retraction or concern on record1000.2020.0OpenAlex, Crossref is_retracted / update-to / updated-by
Impact100
Impact sub-metrics
Sub-metricRaw value0 to 100WeightPointsSource
Field-normalized impactImpact relative to the world average for the same field, year, and type, so a small field and a large field are judged fairly. Triangulates three independent field-normalized measures: OpenAlex's Field-Weighted Citation Impact, its citation percentile within the exact field-and-year cohort, and the NIH iCite Relative Citation Ratio. 1.0x is average; the percentile is the share of same-field, same-year work it out-cites.Maps to 0 to 100: Each ratio (1.0 = field average) maps 1.0 to 50, 3.0 to 75, 9.0 to 90; the field+year percentile maps directly (top 1% -> ~99). The available measures are averaged. When none is available yet (very recent work), this sub-metric is left out and Impact is computed from the others.4099.4x FWCI, 76.8x RCR, top 0.0% of field-year vs field1000.4544.8OpenAlex, NIH iCite fwci / citation_normalized_percentile / relative_citation_ratio
Influential citationsCitations that genuinely build on the work rather than mention it in passing (Semantic Scholar's influential-citation measure). A sharper signal of real impact than a raw count.Maps to 0 to 100: Saturating: 15 influential citations maps to 50, and none maps to 0. When Semantic Scholar has no measure for the work, this sub-metric is left out and Impact is computed from the others.967 influential990.2019.7Semantic Scholar influentialCitationCount
Citation velocityCitations received in the trailing twelve months (estimated from yearly citation counts: this year's so far plus the share of last year's that falls inside the window), judged as a rate rather than a lifetime total. Momentum is what a frontier index cares about: a result being taken up fast right now is landing, whether its lifetime pile is large or still small. Where an expected field citation rate is known, the momentum is also read relative to it, so a fast-moving result in a quiet field is not overlooked.Maps to 0 to 100: Saturating: 30 citations in the last twelve months maps to 50. When NIH iCite provides the field's expected rate, this is blended evenly with the ratio of observed to expected momentum.10,452 in last 12 months (1228.2x field rate)1000.3534.9OpenAlex, NIH iCite counts_by_year / field_citation_rate
Novelty43
Novelty sub-metrics
Sub-metricRaw value0 to 100WeightPointsSource
Conceptual noveltyHow genuinely new the contribution is, measured at publication from the work's OWN content, not its date: how atypical its combination of research areas is versus all prior work, and whether it builds on a broad, deep base rather than only the newest papers in a fast-moving area. This is what separates a real advance from a recent-but-commodity result, and it needs no forward citations, so it is stable for brand-new work. An honestly noisy proxy (even state-of-the-art novelty measures agree only moderately with expert judgment), so it carries bounded weight and never drives the ranking alone.Maps to 0 to 100: Two publication-time signals, each 0-100. The primary one is atypical combination: how unusual the work's median pairing of research topics is versus the prior literature, by pointwise mutual information (a pairing made far more often than chance scores low). The second, foundational reach (the median age of the work's references and the share older than five years), can only lift it: when reach is higher, the score rises by 35% of the gap, and it is never lowered. When only one signal is measurable it is used alone; when neither is (a work too thin in topics or references), this sub-metric is left out and Novelty is computed from the other three. A review is damped to 60%.Median topic pairing Reinforcement Learning in Robotics x Explainable Artificial Intelligence (XAI) (2,243 prior works made this pairing); builds on refs a median 1.6y deep220.4510.0OpenAlex topics + referenced_works (publication-time)
RecencyHow recently the work appeared. The frontier is now, so newer scores higher.Maps to 0 to 100: Exponential decay with a 12-month half-life from the publication date.14 months old450.3013.5OpenAlex publication_date
Attention accelerationWhether attention is rising: citations in the latest full year versus the year before. Accelerating interest signals an active, opening frontier.Maps to 0 to 100: Latest/prior-year ratio: 1x maps to 50, 2x to 75, 4x to 100. The prior year counts as at least 3 citations, so a jump from one citation to a few is not read as explosive growth. With no citations in the latest full year it reads as unknown, and unknown is neutral (50).70.9x rising1000.1010.0OpenAlex counts_by_year
Edge of reviewPreprints sit ahead of the peer-review process and brand-new papers have only just cleared it. Both earn novelty here (while a preprint is discounted under Evidence), because that is where the frontier forms first.Maps to 0 to 100: Preprint 95; any other record 80 when under 6 months old, 65 under 18 months, 45 after that.Peer-reviewed record650.159.8OpenAlex type / publication_date
ProvenanceAll sources

Sources and data

The source figures behind this entry, grouped by the source that holds them: up to five citation indices cross-checked, field-normalized impact, then real-world reach (Wikipedia, community discussion, clinical trials, released data and public funding) where it exists. Each value links to its record.

OpenAlex

Primary record
Total citations
16,824
Field-weighted citation impact
4099.4x field average
Citation percentile
Top 0.1% of its field-year
Citations, recent 12 months
10,452
Publication date
2025-09-17

Crossref

Citations
1,471
Works it builds on
36

Semantic Scholar

Citations, all versions
6,077
Influential citations
967

OpenCitations

Citations
478

Europe PMC

Citations
273

NIH iCite

Relative Citation Ratio
76.77 (NIH-funded median 1.0)
Potential to translate
95%
Cited by clinical articles
1

Unpaywall

Reuse license
CC-BY

Wikipedia

Wikipedia articles citing it
5
For the authors

Show this score where your work lives

A badge for your lab page, your CV or the paper's code repository. It links readers to this entry, and it always carries the entry's current score.
This paper's Frontier Score badge, as it appears where you place itLive preview
<a href="https://frontierresearch.co/breakthroughs/deepseek-r1-reasoning-learned-through-pure-reinforcement-learning?utm_source=badge&amp;utm_medium=badge&amp;utm_campaign=author-kit"><img src="https://frontierresearch.co/api/badge/W4414281281.svg" alt="Frontier Score for this paper" height="20"></a>

For a lab or personal website, a CV page, or anywhere that takes HTML.

The number is live. The badge is drawn from the same signals as this page, so it never shows a score the entry does not. Every entry is re-checked weekly against the same public sources, and a result's standing eases as it ages, so the active edge of the field stays on top. How scoring works

Your other papers. The free paper check shows any paper's field-weighted citation impact, its citations in five open indexes and any retraction on record, whether or not it is in the index, and the researcher check does it for your whole record from your ORCID iD.

Something wrong or out of date? Write to hello@frontierresearch.co and the entry will be corrected.

AI & ComputingThe whole field

Back to the Frontier Index, or browse every breakthrough by date.

The Frontier Brief

The week's AI & Computing, scored.

Every Monday, up to six of the week's new AI & Computing entries in the index, each with its score and a one-line read on why it matters, then the week's best from the rest of the frontier. What you get

Prefer a feed reader? Follow AI & Computing by RSS

One email a week: up to six of the week's new AI & Computing entries, scored, then the best elsewhere. Unsubscribe anytime.