Scaffold-Agent

Long, multi-turn agentic coding sessions for supervised fine-tuning. 19,652 records from 6,792 sessions (1,005.1M tokens, 898.7M trainable after masks). Every assistant action is preceded by a scaffold reasoning block (Inventory → Interaction → Execution) in reasoning_content. Sessions cover editing real repositories and building from scratch, including recovery from errors and demanding simulated users. Sessions run 10 to 100+ turns.

Related datasets

• Specific-Labs/Scaffold-Chat — 3,450,159 chat examples carrying the same scaffold reasoning format, with no tool use: chat and reasoning only. Scaffold Chat (formerly Scaffold-CoT) removed its tool-calling examples because those tool calls were generated text that was never executed. The tool calls in Scaffold-Agent are the opposite: every one was really executed in the Pi harness, and session outcomes were verified with real builds and tests. Train chat/reasoning behavior from Scaffold Chat and tool use from here.

Built from the ScaffoldAgent Experiment 2 generation pipeline (teacher: stealth/space-bunny-alpha through a scaffold-reframing gateway; harness: Pi 0.81.1). See training_sft_config.yaml in this repo for the documented multi-turn SFT setup (sample definition, role/loss mapping, splits).

Fields

Every row is one compaction segment (1–8 per session, each ≤ 64k tokens) and is a self-contained training sample — do not join segments of a session.

Field	Contents
id	{session}-s{segment} record id
session	Session id (repo-… = live-repo work, scratch-… = from-scratch work)
segment / segments_in_session	Position of this record within its session
tokens / chars	o200k_base token count and character count of the record
counter	Tokenizer used for tokens (o200k_base)
meta	{task_id, persona, outcome, verify_exit_code} — simulated-user persona and verification outcome for the session
tools	Function definitions available in this segment (render to tool schema; never train as text)
messages	Ordered chat messages: system (once), user (task + simulated user), assistant (with tool_calls, reasoning_content, and per-message loss), tool (execution results)

Assistant messages carry loss: {content, reasoning}. Train content only when loss.content is true and reasoning only when loss.reasoning is true; everything else is context. The main shard (916M tokens) is masked by a two-judge turn filter (3.4% of content, 30.9% of reasoning masked as contradicted or unfaithful). The campaign22 shard (89M tokens) was admitted under relaxed gates (verification only, no grade bar) and is fully unmasked.

Build

Selected by hidden-test verification plus a senior-reviewer code grade (mergeable verdict, correctness/readability/idiom/scope floors, no test gaming; from-scratch bug claims checked by execution). Main shard: 6,444 sessions. Campaign22 shard: 348 verified sessions. Per-message loss masks from two independent turn judgments on the main shard. Split train/val by session id, never by record — segments of one session share history.

Loading

from datasets import load_dataset
ds = load_dataset("Specific-Labs/Scaffold-Agent", split="train")
rec = ds[0]
trainable = [m for m in rec["messages"]
             if m["role"] == "assistant" and (m["loss"]["content"] or m["loss"]["reasoning"])]

Each record trains standalone: system prompt, tool definitions, and message history are all present in the row. Render reasoning_content through the model's reasoning channel, or flatten as <think>…</think> for plain chat models, following loss.reasoning either way.