Akahsizrr/devin-cli-reasoning-distillation

Devin CLI Reasoning Distillation Dataset

A distillation dataset built from Devin CLI session traces, containing the model's internal reasoning traces (chain-of-thought / thinking), user prompts, assistant answers, and tool calls. The dataset is formatted to be directly compatible with SFT training pipelines that expect OpenAI-style message lists with a reasoning_content field.

Dataset Summary

Total rows	2,632 (2,507 train / 125 validation)
Rows with reasoning	2,507 / 2,507 (100% of train)
Format	Parquet (32 columns, matches r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
Source	Devin CLI sessions.db (local agent traces)
Schema version	1.0.0

Teacher Models

This is a multi-teacher distillation dataset. The dominant teacher is GLM-5.2 (70%), with contributions from Devin's own SWE-1.7 and several other frontier models:

Model	Rows	%
glm-5-2	1,758	70.2%
swe-1-7	405	16.2%
glm-5-3-flash-max	168	6.7%
gpt-5-6-luna-xhigh	69	2.8%
deepseek-v4-flash-max	25	1.0%
gpt-5-6-luna-high	24	1.0%
gemini-3-7-flash-high	23	0.9%
glm-5-3-flash-low	18	0.7%
claude-opus-5-high	10	0.4%
claude-sonnet-5-high	7	0.3%

Dataset Structure

Each row represents a conversation segment (anchored to a single user prompt) containing:

• The full message history (system prompts, user input, assistant responses, tool calls, tool results)
• The assistant's reasoning trace in the reasoning_content field — the model's internal chain-of-thought before producing its answer
• Tool calls in OpenAI function-calling format

Key Columns

Column	Type	Description
messages_json	string	JSON array of messages, each with content, reasoning_content, role, tool_calls, tool_call_id, name, trainable
teacher_model	string	The model that generated the response
teacher_provider	string	Always cognition
trace_kind	string	single_turn or multi_turn
n_assistant_turns	int32	Number of assistant turns in the segment
n_tool_calls	int32	Number of tool calls in the segment
domain	string	Inferred domain (coding, reasoning, math)
source_item_id	string	{session_id}#{segment_index} for traceability

Message Format

Each message in messages_json follows this structure:

{
  "content": "The visible answer or prompt text",
  "reasoning_content": "The model's internal thinking trace (chain-of-thought)",
  "role": "system|user|assistant|tool",
  "tool_calls": [{"id": "...", "type": "function", "function": {"name": "...", "arguments": "..."}}],
  "tool_call_id": "",
  "name": "",
  "trainable": true
}

• trainable: true for assistant messages (the target for distillation)
• trainable: false for system, user, and tool messages (context only)
• reasoning_content is populated for assistant messages that include a thinking trace

Use Cases

• SFT distillation: Train a student model to reproduce the reasoning + answer patterns
• Reasoning training: Teach chain-of-thought reasoning with tool use
• Agent training: Multi-turn agentic workflows with tool calls
• Multi-teacher distillation: Blend knowledge from GLM-5.2, SWE-1.7, and other models

Statistics

Metric	Value
Sessions	530
Avg assistant turns per segment	9.0
Avg tool calls per segment	9.7
Single-turn segments	2,479 (99%)
Multi-turn segments	28 (1%)

Provenance

• Source: Devin CLI agent sessions running on a local Windows workstation
• Collection period: July 2026 – September 2026
• Tasks: Software engineering, code debugging, ML training, research, general Q&A
• Extraction: Conversations reconstructed from sessions.db message chain, split at user-turn boundaries, filtered to segments containing at least one reasoning trace

License

This dataset contains traces from proprietary AI models accessed via the Devin CLI platform. Usage is subject to the terms of service of the respective model providers.