reasoning capabilities - chunhualiao/public-docs GitHub Wiki

In the DeepSeek-R1 paper, the term reasoning capabilities refers to a model's ability to perform structured, logical, and multi-step thinking to arrive at solutions for complex problems across a wide range of domains. While tasks like coding or solving math problems involve reasoning, the paper distinguishes reasoning capabilities more broadly as foundational skills applicable across diverse domains, transcending any specific task.

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Reasoning capabilities in this paper are described as the model's ability to:

1. Generate Long Chains of Thought (CoT): Produce structured, step-by-step explanations that reflect its thinking process.
2. Self-Verification: Evaluate its own intermediate steps and results, revisiting errors and improving answers autonomously.
3. Handle Complex Multi-Step Tasks: Navigate problems requiring multiple layers of reasoning (e.g., combining logical, mathematical, or linguistic elements).
4. Adapt to General Domains: Perform well not only in specific tasks (e.g., coding, math) but also in diverse areas like factual question answering, logic puzzles, or language tasks.

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Reasoning capabilities extend beyond the mechanics of solving domain-specific problems. Here's how they differ:

• Reasoning: Broadly applies to any task requiring logical thinking, problem-solving, or decision-making, whether it's a math problem, a coding task, or a complex argument.
  • Example: Explaining why a particular mathematical theorem holds or why a programming approach is optimal.
• Math/Coding Tasks: Typically involve applying specific domain rules (e.g., solving equations, debugging code) rather than reasoning across contexts.

• Reasoning: Emphasizes the process of thinking, where clarity and coherence in intermediate steps are as important as the final answer.
  • Example: For the question:

    Prove that the sum of angles in a triangle is 180 degrees.
    

    A reasoning-capable model would output:

    <think> To prove this, we start with the fact that the sum of angles on a straight line is 180°. Next, consider a triangle with angles A, B, and C. Extend one side of the triangle... </think> Answer: The sum is 180°.
    

• Math/Coding Tasks: May focus more on reaching the correct solution without emphasizing a clear explanation or step-by-step logic.

• Reasoning: The paper highlights behaviors like reflection and self-correction, which are emergent from the reinforcement learning setup and are not task-specific.
  • Example: Revisiting earlier reasoning steps when errors are detected.
• Math/Coding Tasks: Often do not inherently require or demonstrate self-reflection. Success is measured by task-specific correctness (e.g., passing test cases for a code problem).

• Reasoning: Applies to abstract or loosely defined problems that may not have a single correct solution.
  • Example: Answering open-ended philosophical questions or interpreting ambiguous scenarios.
• Math/Coding Tasks: Are constrained by clear domain rules, often with deterministic solutions.

• Reasoning: In the training pipeline, reasoning is incentivized using rewards for:
  • Accuracy.
  • Coherence in reasoning steps.
  • Readability and consistency (e.g., avoiding language mixing).
• Math/Coding: Rewards are often based solely on task-specific outcomes (e.g., whether a math problem is solved correctly or code compiles and runs as intended).

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Reasoning capabilities represent a higher-order skill set enabling LLMs to generalize logical processes across domains, including but not limited to coding and math. These capabilities focus on process, adaptability, and coherence, whereas domain-specific tasks emphasize solving problems within fixed rules or structures. The paper underscores that reasoning is the foundation underlying performance across all these tasks.