stindardlogic/agent-planning-sft-50k

Agent Planning SFT (50K)

50,000 ShareGPT-format conversations demonstrating ReAct-style agent reasoning: explicit Thought -> Action -> Observation cycles leading to grounded Final Answers across research, coding, data analysis, troubleshooting, planning, writing, and estimation tasks.

Motivation

Most LLM training data shows answers, not the reasoning process that produces them. Agentic systems need models that can:

• Decompose complex tasks into concrete sub-steps
• Identify what information they need before answering
• Verify their reasoning with explicit observations
• Synthesize multiple findings into a coherent response
• Flag uncertainty and ask clarifying questions

This dataset trains on the full cognitive process, not just the final output.

Dataset Description

50,000 conversations across 7 task types and 13 domains:

Task Type Distribution

Task Type	Count	%
Coding	~15,400	31%
Research	~11,500	23%
Planning	~7,700	15%
Data Analysis	~3,900	8%
Writing	~3,900	8%
Reasoning	~3,900	8%
Troubleshooting	~3,900	8%

Domain Coverage

Factual lookup, comparative analysis, implementation, debugging, architecture, SQL analysis, production incident response, project planning, email drafting, Fermi estimation, decision support, code review, and technical roadmapping.

Format

Standard ShareGPT format with ReAct-style reasoning in the assistant turn:

{
  "conversations": [
    {
      "from": "human",
      "value": "How many piano tuners are there in Chicago?"
    },
    {
      "from": "gpt",
      "value": "Thought: This is a Fermi estimation problem. I need to estimate from first principles...\n\nAction: Estimate Chicago population\nObservation: Chicago city proper ~2.7M people...\n\nAction: Estimate piano ownership rate\nObservation: ~2.5% of urban households own pianos...\n\nFinal Answer: 25-50 piano tuners in Chicago city, 75-150 in the metro area."
    }
  ],
  "metadata": {
    "task_type": "reasoning",
    "domain": "estimation"
  },
  "id": "abc123"
}

ReAct Pattern Demonstrated

Every assistant response follows the Thought -> Action -> Observation cycle:

Thought: What do I need to figure out? What are the key sub-problems?

Action: A concrete step taken (search, calculate, analyze, compare, write)

Observation: What was learned from that action - factual, not conclusory

Final Answer: Synthesis of all observations into a direct, well-structured response

This pattern trains models to reason transparently before committing to conclusions.

Task Categories in Detail

Coding tasks:

• Implementation: write a thread-safe token bucket rate limiter
• Debugging: diagnose async ordering issues, trace execution paths
• Architecture: design webhook processing for 10k events/minute
• Code review: identify SQL injection, resource leaks, antipatterns

Research tasks:

• Comparative analysis: PostgreSQL vs MongoDB database selection
• Decision support: self-hosted ML vs managed API evaluation
• Factual lookup: LLC vs LP business entity comparison

Planning tasks:

• Project planning: monolith to microservices migration strategy
• Technical roadmap: test coverage improvement from 23% to 80%

Data Analysis:

• SQL analysis: consecutive 7-day login streak query (gaps and islands)

Troubleshooting:

• Production incident: API latency spike diagnosis without deployment

Writing:

• Email drafting: scope reduction announcement to stakeholders

Reasoning:

• Estimation: Fermi problem (piano tuners in Chicago)

Key Training Signals

1. Explicit decomposition: Problems are broken into numbered action steps before the final answer appears. Models learn to plan rather than pattern-match to the first plausible response.

2. Action specificity: Each action is concrete and targeted ("Check for SQL injection vulnerability" not "look at the code"). Observations confirm or disconfirm specific hypotheses.

3. Calibrated confidence: Observations include uncertainty. The model acknowledges when assumptions are rough and provides ranges rather than false precision.

4. Synthesis quality: Final answers integrate all observations into a structured, actionable response - not just a list of findings.

5. Question-back pattern: Complex decisions end with a clarifying question to refine the recommendation, demonstrating that good agents identify missing information.

Use Cases

• SFT fine-tuning for agentic AI assistants
• Training models to show reasoning work (chain-of-thought)
• Building AI systems for complex multi-step task completion
• Research on planning and decomposition in language models
• Training models to use tools and synthesize multi-source information
• Developer assistant products requiring debugging and code review

License

Apache 2.0