| --- |
| 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](https://huggingface.co/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 |
|
|
| ```python |
| 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. |
|
|
