> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Reasoning

> Reasoning techniques for LLMs and VLMs including chain-of-thought, tool use, ReAct, and visual reasoning.

This section covers emergent reasoning capabilities in large and vision-language models, and how to elicit and extend them through structured prompting and interaction patterns.

## LLM Reasoning

<CardGroup cols={2}>
  <Card title="Chain-of-Thought" icon="link">
    Step-by-step reasoning that prompts LLMs to articulate intermediate reasoning steps before arriving at a final answer.
  </Card>

  <Card title="Tool Use" icon="wrench">
    Extending LLM capabilities with external tools such as calculators, search engines, and code interpreters.
  </Card>

  <Card title="ReAct" icon="rotate">
    Interleaving reasoning traces and actions, enabling LLMs to dynamically plan and interact with environments.
  </Card>
</CardGroup>

## Lab

<CardGroup cols={2}>
  <Card title="RL for Reasoning in Small LLMs" icon="flask" href="https://github.com/knoveleng/open-rs">
    AAAI 2026 — Fine-tune DeepSeek-R1-Distill-Qwen-1.5B with GRPO on a compact math dataset. AMC23 accuracy improves from 63% to 80%; AIME24 reaches 46.7%, surpassing o1-preview. Full training run costs \~\$42 on 4× A40 GPUs.
  </Card>
</CardGroup>

## VLM Reasoning

<CardGroup cols={2}>
  <Card title="VLM Reasoning" icon="eye" href="/aiml-common/lectures/reasoning/vlm-reasoning/index">
    Visual chain-of-thought and grounded tool use for vision-language models.
  </Card>
</CardGroup>

## Further reading

* Hugging Face — [LLM Course, Chapter 12: Reasoning](https://huggingface.co/learn/llm-course/en/chapter12/1)

<img src="https://mintcdn.com/aegeanaiinc/YTWk2_ZSjZxjr10l/aiml-common/lectures/reasoning/images/rl-reasoning-pipeline.png?fit=max&auto=format&n=YTWk2_ZSjZxjr10l&q=85&s=c36f20cb57576abe26666cedbb30480a" alt="RL-for-reasoning training pipeline: (1) Generation — a batch of sequences is rolled out by Generators under policy πᵢ; (2) Scoring — Verifiers judge each completion and emit a reward; (3) Batching — scored sequences from the current policy are assembled into the step-i batch; (4) Weight update — Trainers take the batch and produce the next policy πᵢ₊₁ = πᵢ + ∇J. Dotted lines mark policy changes between batches; faded horizontal tracks carry over from previous policies." width="1229" height="600" data-path="aiml-common/lectures/reasoning/images/rl-reasoning-pipeline.png" />

*RL-for-reasoning training loop: the four canonical stages (Generation → Scoring → Batching → Weight update) that every GRPO / RLHF / RLAIF pipeline implements.*

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