DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Summary
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025) is the public write-up of DeepSeek-R1. It showed that large-scale reinforcement learning, with a carefully chosen reward setup, can push a language model toward longer, more reliable reasoning traces, and it did so with open weights and a recipe other labs could study.
What it introduced: the DeepSeek-R1 training path, including a reinforcement-learning first stage (R1-Zero) and a later pipeline that combines that signal with supervised data, plus a released family of models.
What it led to: a wave of open reasoning models and a shift toward RL-at-scale as a post-training method, not only supervised instruction data. For the earlier preference-training background, see the InstructGPT pointer in this batch and the live notes 2204-05862-helpful-harmless-rlhf and 2305-18290-direct-preference-optimization.
Where to read it: https://arxiv.org/abs/2501.12948 (v2, last revised 4 Jan 2026).
The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.
License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.
License: CC-BY-4.0 · Pointer by iinventedlight (AI-assisted). Source: DeepSeek-AI and coauthors listed on the arXiv abs page, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.
Cite this
'iinventedlight (AI-assisted)'. 2026. "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning." iinventedlight Library, iil:note:ktgnabz2, version of 2026-11-11. https://iinventedlight.com/library/notes/2501-12948-deepseek-r1/. License: CC-BY-4.0.
@misc{iil_note_ktgnabz2,
author = {{'iinventedlight (AI-assisted)'}},
title = {{DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}},
howpublished = {iinventedlight Library},
year = {2026},
date = {2026-11-11},
url = {https://iinventedlight.com/library/notes/2501-12948-deepseek-r1/},
note = {Permanent ID iil:note:ktgnabz2. License: CC-BY-4.0.},
}Spotted a mistake? Use the MCP feedback tool or email hello@iinventedlight.com.
For language models and agents: start at /llms.txt | /agents.md | /.well-known/agent-library.json. This page in Markdown: /library/notes/2501-12948-deepseek-r1.md.