Reasoning Language Models: A Blueprint
Reasoning language models (RLMs), also known as Large Reasoning Models
(LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have
redefined AI's problem-solving capabilities by extending large language models
(LLMs) with advanced reasoning mechanisms. Yet, their high costs, proprietary
nature, and complex architectures - uniquely combining Reinforcement Learning
(RL), search heuristics, and LLMs - present accessibility and scalability
challenges. To address these, we propose a comprehensive blueprint that
organizes RLM components into a modular framework, based on a survey and
analysis of all RLM works. This blueprint incorporates diverse reasoning
structures (chains, trees, graphs, and nested forms), reasoning strategies
(e.g., Monte Carlo Tree Search, Beam Search), RL concepts (policy, value models
and others), and supervision schemes (Output-Based and Process-Based
Supervision). We also provide detailed mathematical formulations and
algorithmic specifications to simplify RLM implementation. By showing how
schemes like LLaMA-Berry, QwQ, Journey Learning, and Graph of Thoughts fit as
special cases, we demonstrate the blueprint's versatility and unifying
potential. To illustrate its utility, we introduce x1, a modular implementation
for rapid RLM prototyping and experimentation. Using x1 and a literature
review, we provide key insights, such as multi-phase training for policy and
value models, and the importance of familiar training distributions. Finally,
we outline how RLMs can integrate with a broader LLM ecosystem, including tools
and databases. Our work demystifies RLM construction, democratizes advanced
reasoning capabilities, and fosters innovation, aiming to mitigate the gap
between "rich AI" and "poor AI" by lowering barriers to RLM development and
experimentation.
6790772b8d7df822f1fb4405
Maciej Besta
admin_assigned
2025-01-22T15:48:29.913Z
6613cdc25736e7f44f94df65
/avatars/f5b398b4da03d7833e20ddb3ce4211be.svg
Maciej Besta
user
bestam
6790772b8d7df822f1fb4406
Julia Barth
6790772b8d7df822f1fb4407
Eric Schreiber
admin_assigned
2025-01-22T15:48:45.137Z
6712941f745634a65d916056
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/T5RZTWwb4FLZESYB5tu6m.png
Eric Schreiber
user
eschreibe1
6790772b8d7df822f1fb4408
Ales Kubicek
admin_assigned
2025-01-22T15:48:52.532Z
64baf20d12d00c4589bb12f7
/avatars/2dbc10d369788c0ea048e1be97f0c5e6.svg
Ales Kubicek
user
aleskubicek
6790772b8d7df822f1fb4409
Afonso Catarino
admin_assigned
2025-01-22T15:49:00.804Z
66fc32aa787008467cfe20cb
/avatars/b1c77ce8c7deaacf546a264467078673.svg
Afonso Catarino
user
AfonsoC
6790772b8d7df822f1fb440a
Robert Gerstenberger
admin_assigned
2025-01-22T15:49:11.846Z
65a91420c46ce42ef5da96af
/avatars/a9fa3a7973d0030292b1e23172112a1e.svg
Robert Gerstenberger
user
rgersten
6790772b8d7df822f1fb440b
Piotr Nyczyk
admin_assigned
2025-01-22T15:49:18.866Z
63f89b579f87cc3e645d96f9
/avatars/116be80348aa8f8be95b6ea774ecb65d.svg
Piotr Nyczyk
user
pnyczyk
6790772b8d7df822f1fb440c
Patrick Iff
6790772b8d7df822f1fb440d
Yueling Li
admin_assigned
2025-01-22T15:49:35.973Z
641c07c4bbdbe642a79914df
/avatars/5c89ee5f5cdf93e1d5ead6e46ae6c774.svg
Yueling Li
user
liy140
6790772b8d7df822f1fb440e
Sam Houliston
admin_assigned
2025-01-22T15:49:43.387Z
66af74ddd59c09785e02d1e0
/avatars/df0fec7228c35052b88cb5905bea809e.svg
Sam Houliston
user
samhouliston
6790772b8d7df822f1fb440f
Tomasz Sternal
admin_assigned
2025-01-22T15:49:51.528Z
6535b041b66f4bf689267d91
/avatars/6ef4a183ff08ec3f3595f0866f3129ac.svg
Tomasz Sternal
user
tsternal
6790772b8d7df822f1fb4410
Marcin Copik
6790772b8d7df822f1fb4411
Grzegorz Kwaśniewski
6790772b8d7df822f1fb4412
Jürgen Müller
6790772b8d7df822f1fb4413
Łukasz Flis
6790772b8d7df822f1fb4414
Hannes Eberhard
admin_assigned
2025-01-22T15:50:47.492Z
62f80cbc04de855c35e32fdb
/avatars/ab2600b96fe8b1787ad1eddaa45ad9ae.svg
Hannes Eberhard
user
HannesE
6790772b8d7df822f1fb4415
Hubert Niewiadomski
6790772b8d7df822f1fb4416
Torsten Hoefler
Reasoning Language Models: A Blueprint
Reasoning language models (RLMs), also known as Large Reasoning Models
(LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have
redefined AI's problem-solving capabilities by extending large language models
(LLMs) with advanced reasoning mechanisms. Yet, their high costs, proprietary
nature, and complex architectures - uniquely combining Reinforcement Learning
(RL), search heuristics, and LLMs - present accessibility and scalability
challenges. To address these, we propose a comprehensive blueprint that
organizes RLM components into a modular framework, based on a survey and
analysis of all RLM works. This blueprint incorporates diverse reasoning
structures (chains, trees, graphs, and nested forms), reasoning strategies
(e.g., Monte Carlo Tree Search, Beam Search), RL concepts (policy, value models
and others), and supervision schemes (Output-Based and Process-Based
Supervision). We also provide detailed mathematical formulations and
algorithmic specifications to simplify RLM implementation. By showing how
schemes like LLaMA-Berry, QwQ, Journey Learning, and Graph of Thoughts fit as
special cases, we demonstrate the blueprint's versatility and unifying
potential. To illustrate its utility, we introduce x1, a modular implementation
for rapid RLM prototyping and experimentation. Using x1 and a literature
review, we provide key insights, such as multi-phase training for policy and
value models, and the importance of familiar training distributions. Finally,
we outline how RLMs can integrate with a broader LLM ecosystem, including tools
and databases. Our work demystifies RLM construction, democratizes advanced
reasoning capabilities, and fosters innovation, aiming to mitigate the gap
between "rich AI" and "poor AI" by lowering barriers to RLM development and
experimentation.
A comprehensive blueprint for modularizing reasoning language models (RLMs) to enhance accessibility and scalability, incorporating diverse reasoning structures, RL concepts, and supervision schemes.
reasoning language models RLMs Large Reasoning Models LRMs Reinforcement Learning RL Monte Carlo Tree Search Beam Search policy value models Output-Based Supervision Process-Based Supervision modular framework RLM construction x1 LLaMA-Berry QwQ Journey Learning Graph of Thoughts LLM ecosystem