# Rozumowanie w modelach językowych

EN: **Reasoning in language models** · id `reasoningmodels`

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › [Sieci i modele generatywne](https://aiatlasgraph.com/t/neural.md) › Rozumowanie w modelach językowych

## Description

- PL: Techniki i rodziny modeli służące do zadań wymagających wieloetapowego rozumowania: od promptowania łańcucha myśli, przez większy budżet obliczeń podczas odpowiedzi, po trening modeli rozumujących (RL, SFT i destylację).
- EN: Techniques and model families for tasks that require multi-step reasoning: from chain-of-thought prompting and larger inference-time compute budgets to training reasoning models with RL, SFT and distillation.

## In one sentence

- PL: To sposoby pomagające modelom językowym rozwiązywać zadania wymagające kilku powiązanych kroków.
- EN: These methods help language models solve tasks that require several connected steps.

## Problem it solves

- PL: Pierwsza szybka odpowiedź może pominąć warunek, błędnie policzyć wynik lub nie połączyć ważnych informacji.
- EN: A quick first response may miss a condition, miscalculate a result or fail to connect important information.

## What it does

- PL: Model może ćwiczyć rozwiązywanie zadań, rozpisywać kroki, sprawdzać warianty i poświęcać więcej obliczeń; poprawność nadal wymaga oceny.
- EN: A model can practise solving tasks, produce intermediate steps, check alternatives and spend more computation; correctness still needs evaluation.

## Everyday example

- PL: Prosisz o plan pakowania zamówień z ograniczeniami rozmiaru, a model sprawdza kilka układów przed zaproponowaniem rozwiązania.
- EN: You request an order-packing plan with size constraints, and a model checks several arrangements before proposing a solution.

## This concerns you if you…

- PL: zadania wymagają łączenia kilku warunków
- PL: szybkie odpowiedzi często pomijają istotne kroki
- EN: handle tasks with several connected constraints
- EN: find quick responses often omit important steps

## Keywords

chain-of-thought · test-time compute · DeepSeek-R1 · reasoning traces · process reward models · outcome verification · self-consistency · verifiable rewards

## How it works

- PL: Model tworzy pośrednie reprezentacje lub tekstowe kroki, których kontekst wpływa na dalsze przewidywania. Promptowanie, trening z oceną rozwiązań oraz wybór między kandydatami zmieniają procedurę rozwiązywania zadania i przydział obliczeń.
- EN: A model produces intermediate representations or textual steps that condition subsequent predictions. Prompting, solution-scored training and selection among candidates alter task-solving procedures and computation allocation.

## Limits

- PL: Dłuższa ścieżka nie gwarantuje poprawności ani wiernego opisu wewnętrznych obliczeń. Błędy mogą się kumulować; zysk wymaga odpowiednich zadań, danych i wiarygodnej oceny rozwiązań.
- EN: Longer traces guarantee neither correctness nor faithful descriptions of internal computation. Errors can accumulate; gains require suitable tasks, data and reliable solution evaluation.

## Key terms

- PL: **ślad rozumowania**: Sekwencja kroków pośrednich prowadząca do odpowiedzi modelu.
- PL: **weryfikator**: Program lub model oceniający kandydackie rozwiązanie albo jego fragment.
- EN: **reasoning trace**: A sequence of intermediate steps leading to a model's answer.
- EN: **verifier**: A program or model scoring a candidate solution or part of it.

## Know first

- [Transformery i attention (Transformers and attention)](https://aiatlasgraph.com/t/transformers.md)
- [Zbiory testowe i regresje (Evaluation datasets and regression tests)](https://aiatlasgraph.com/t/evalsets.md)

## Examples

- PL: chain-of-thought · test-time compute · tryb „thinking” · modele typu o1/o3 · DeepSeek-R1
- EN: chain-of-thought · test-time compute · “thinking” mode · o1/o3-style models · DeepSeek-R1

## Subtopics (7 direct, 26 in total)

- [Efektywne rozumowanie (przeciw „overthinkingowi”) (Efficient reasoning)](https://aiatlasgraph.com/t/efficientreasoning.md): Methods that cut the cost and latency of reasoning: a model should not write thousands of thinking tokens for a simple question.
  - [Adaptacyjne i hybrydowe tryby myślenia (Adaptive and hybrid thinking modes)](https://aiatlasgraph.com/t/adaptivethinking.md): One model can answer fast or think longer and decides by itself (or on request) how much reasoning a question needs.
  - [Skracanie łańcucha myśli (Reasoning length reduction)](https://aiatlasgraph.com/t/concisereasoning.md): Training or prompting aimed at reducing reasoning length while preserving answer quality as much as possible — for example via a length-cost signal, fine-tuning on concise traces, or brevity instructions.
- [Monitorowanie łańcucha myśli (Chain-of-thought monitoring)](https://aiatlasgraph.com/t/cotmonitoring.md): Analysing a model's written chain of thought (automatically or by a human) to detect signals of undesirable behaviour. Its usefulness depends in part on the monitorability and faithfulness of the reasoning trace, which are not guaranteed.
- [Obliczenia w czasie wnioskowania (Test-time compute scaling)](https://aiatlasgraph.com/t/testtime.md): Giving a model a bigger compute budget at answer time — without changing weights: many attempts in parallel, successive revisions, verifier-guided search, or simply thinking longer.
  - [Iteracyjne poprawianie odpowiedzi (Sequential self-revision)](https://aiatlasgraph.com/t/sequentialrevision.md): The model reviews and revises its own answer in turns, each version building on the previous — compute grows in depth rather than width.
  - [Przeszukiwanie kroków z weryfikatorem (Verifier-guided search)](https://aiatlasgraph.com/t/verifiersearch.md): The solution is built step by step while a verifier (e.g. a process reward model) scores partial paths and decides which to expand.
  - [Sterowanie budżetem myślenia (Thinking budget control)](https://aiatlasgraph.com/t/budgetcontrol.md): Explicitly setting how many tokens or how much time the model may spend reasoning — via a parameter, a cap, or forcing it to stop or continue thinking.
  - [Wiele prób i głosowanie (Parallel sampling and voting)](https://aiatlasgraph.com/t/parallelsampling.md): The model generates many independent solutions in parallel; the answer is picked by majority vote (self-consistency) or by a verifier score (best-of-N).
- [Pętle głębokości i myśli ukryte (Recurrent depth and latent thoughts)](https://aiatlasgraph.com/t/latentloops.md): Scaling answer-time compute by repeating a block or a continuous state, not by extra tokens.
  - [Adaptacyjne zatrzymanie obliczeń (Adaptive computation time)](https://aiatlasgraph.com/t/acthalt.md): A differentiable rule that teaches a recurrent network how many steps to take between input and output.
  - [Ciągłe myśli (Continuous chain of thought)](https://aiatlasgraph.com/t/coconut.md): A reasoning chain in which the hidden state returns as an embedding, without decoding to a token.
  - [Głębokość rekurencyjna (Recurrent-depth language models)](https://aiatlasgraph.com/t/recurrentdepth.md): A language model scales answer compute by unrolling a shared recurrent block in hidden space.
  - [Transformery w pętli (Looped transformers)](https://aiatlasgraph.com/t/loopedtf.md): A shared transformer block applied recurrently to sequence representations, sometimes with per-position halting.
- [Trenowanie modeli rozumujących (Training reasoning models)](https://aiatlasgraph.com/t/reasoningtraining.md): How to teach a model long, self-correcting reasoning: RL rewarded for correct results, SFT on long chains of thought, and distillation from a larger model.
  - [Destylacja rozumowania do mniejszych modeli (Reasoning distillation)](https://aiatlasgraph.com/t/reasoningdistill.md): A large reasoning model generates reasoning traces that a smaller model is fine-tuned on. A cheap way to give a small model reasoning ability.
  - [RL z weryfikowalnymi nagrodami (RLVR) (Reinforcement learning with verifiable rewards)](https://aiatlasgraph.com/t/rlvr.md): The model receives reward from an automatically verifiable signal — such as answer correctness, passing code tests, or satisfying a checkable condition — rather than relying only on a learned preference model. RLVR is an important post-training approach for reasoning models, especially in domains with unambiguous verification.
    - [Rekurencyjne samoulepszanie agentów terminalowych (Recursive self-improvement of terminal agents)](https://aiatlasgraph.com/t/terminalrsi.md): A post-training method alternating supervised learning on verified successful trajectories with PPO using an inherited value model.
  - [SFT na długich łańcuchach myśli (Long chain-of-thought fine-tuning)](https://aiatlasgraph.com/t/longcotsft.md): Supervised fine-tuning on curated examples with long reasoning traces; often a “cold start” before RL, or a cheap route to a reasoning model from few examples.
- [Weryfikatory rozumowania (Reasoning verifiers)](https://aiatlasgraph.com/t/reasoningverifiers.md): Models or programs that judge whether reasoning is correct. Used both as a training reward and to pick the best answer at inference time.
  - [Ocena każdego kroku (PRM) (Process reward models)](https://aiatlasgraph.com/t/prm.md): A reward model scores each reasoning step separately, not just the final answer. Catches a mistake halfway through a solution.
  - [Sprawdzanie końcowego wyniku (Outcome verification)](https://aiatlasgraph.com/t/outcomeverify.md): Only the final answer is judged: by comparing with a reference, running tests, or an outcome reward model (ORM). Simpler and cheaper than step scoring.
- [Łańcuch myśli (chain-of-thought) (Chain-of-thought prompting)](https://aiatlasgraph.com/t/cot.md): Getting the model to write out intermediate reasoning steps before the answer. The simplest way to improve multi-step tasks — no training required.
  - [Drzewo myśli (Tree of Thoughts) (Tree of Thoughts)](https://aiatlasgraph.com/t/treeofthoughts.md): The model expands several alternative reasoning paths, evaluates which look promising and can backtrack — tree search instead of one line of thought.
  - [Łańcuch myśli z przykładami (Few-shot chain-of-thought)](https://aiatlasgraph.com/t/fewshotcot.md): The prompt includes a few example problems with step-by-step worked solutions; the model imitates that style on a new problem.
  - [„Pomyśl krok po kroku” bez przykładów (Zero-shot chain-of-thought)](https://aiatlasgraph.com/t/zeroshotcot.md): Instead of examples, a short instruction such as “Let's think step by step” makes the model write out its reasoning.

## Related topics

Untyped, symmetric "see also" links.

- [AI neurosymboliczna (Neuro-symbolic AI)](https://aiatlasgraph.com/t/neurosymbolic.md)

## Sources

- [Understanding Reasoning LLMs](<https://magazine.sebastianraschka.com/p/understanding-reasoning-llms>) `raschka-reasoning-llms` · by Sebastian Raschka
- [From System 1 to System 2: A Survey of Reasoning Large Language Models](<https://arxiv.org/abs/2502.17419>) `system1-2-survey-2025` · by Zhong-Zhi Li i in.

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/reasoningmodels · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30