Datasets:
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
tags:
- chain-of-thought
- reasoning
- sft
- tool-use
- coding-agent
- multi-turn
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files: scaffold_agent_sft_final.jsonl
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.