stindardlogic/thinking-traces-sft-100k

Thinking Traces SFT (100K)

100,000 ShareGPT-format conversations where the assistant shows explicit extended reasoning in <thinking> tags before giving a clean, structured final answer. Designed for training R1/o1-style reasoning models that separate the internal scratchpad from the public response.

Motivation

Standard SFT datasets train models to output correct answers. This dataset trains models to reason correctly — showing the full deliberation process before committing to an answer. Key behaviors demonstrated:

• Breaking complex problems into explicit sub-steps before solving
• Catching and correcting errors mid-reasoning ("wait, let me re-check...")
• Distinguishing what the reasoning established vs. what requires outside knowledge
• Producing clean, well-structured final answers that summarize the reasoning

Dataset Description

100,000 conversations across 5 categories and 16 subcategories:

Category Distribution

Category	Count	%
Math	~37,500	37.5%
Knowledge	~25,000	25%
Programming	~18,750	18.75%
Logic	~12,500	12.5%
Writing Analysis	~6,250	6.25%

Subcategories Covered

Math: algebra, probability, geometry, number theory, statistics, word problems

Knowledge: science (Rayleigh scattering, thermodynamics), history (historiographical analysis), economics (GDP/GNP, monetary policy), physics (Newtonian mechanics, quantum basics)

Programming: algorithm implementation (min-heap, palindrome detection), debugging, data structures

Logic: deductive reasoning, constraint puzzles, probability paradoxes (Monty Hall)

Writing: rhetorical analysis, argument structure

Format

{
  "conversations": [
    {
      "from": "human",
      "value": "A bag has 4 red and 6 blue balls. You draw 2 without replacement. What is the probability both are red?"
    },
    {
      "from": "gpt",
      "value": "<thinking>\nI need P(both red) when drawing 2 balls without replacement...\n\nSequential probability approach:\n- P(first ball is red) = 4/10 = 2/5\n- After drawing one red: 3 red, 6 blue = 9 total\n- P(second red | first red) = 3/9 = 1/3\n- P(both red) = (2/5) * (1/3) = 2/15\n\nVerify with combinations: C(4,2)/C(10,2) = 6/45 = 2/15 ✓\n</thinking>\n\n**P(both red) = 2/15 ≈ 13.3%**\n..."
    }
  ],
  "metadata": {
    "category": "math",
    "subcategory": "probability",
    "difficulty": "medium"
  },
  "id": "abc123"
}

Thinking Trace Structure

Each <thinking> block demonstrates:

1. Problem decomposition: Breaks the question into sub-questions before attempting any answer

2. Method selection: Explicitly chooses an approach and explains why (e.g., "I'll use the Euclidean algorithm rather than prime factorization because...")

3. Step-by-step execution: Works through calculations with intermediate values shown

4. Self-verification: Uses an alternative method or spot-check to confirm the answer

5. Error detection: Where relevant, shows a reasoning path that hits a contradiction, then corrects course

6. Calibrated confidence: Notes when results are approximate or depend on assumptions

Key Training Signals

The gap between <thinking> and the final answer is intentional and important:

• Thinking: messy, exploratory, includes dead ends and corrections, uses abbreviations, shows raw arithmetic
• Final answer: clean, formatted, structured for the reader, omits the false starts

This trains models to use extended thinking as a private scratchpad while producing polished public responses — the core behavior of o1-class models.

Reasoning Patterns Demonstrated

• Algebraic manipulation: step-by-step equation solving with substitution/verification
• Combinatorial reasoning: setting up counting problems correctly before computing
• Bayesian updates: tracking how new information changes probability estimates
• Algorithmic tracing: manually tracing code/algorithms on concrete examples
• Causal reasoning: distinguishing correlation, causation, and confounds
• Historiographical reasoning: presenting competing scholarly interpretations, not single "facts"
• Fermi estimation: building up from known quantities with explicit uncertainty

Use Cases

• SFT fine-tuning for o1/R1-style extended reasoning models
• Training models to show their work before answering
• Math and science tutoring applications requiring step-by-step solutions
• Code explanation and debugging assistants
• Research on scratchpad/chain-of-thought training dynamics
• Evaluating reasoning faithfulness (does the thinking match the answer?)

License

Apache 2.0