| --- |
| license: other |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - agentic |
| - software-engineering |
| - tool-use |
| - code |
| - distillation |
| - gasai |
| - slm |
| size_categories: |
| - 1K<n<10K |
| source_datasets: |
| - togethercomputer/CoderForge-Preview |
| pretty_name: Gasai-Agent-CoderForge |
| configs: |
| - config_name: default |
| data_files: train.jsonl |
| --- |
| |
| # Gasai-Agent-CoderForge |
|
|
| **5,205 verified-resolved agentic software-engineering trajectories, re-authored into the Gasai harness |
| format for pretraining small language models.** Derived from `togethercomputer/CoderForge-Preview` |
| (`reward==1`, Python). Each row is one complete tool-use trace serialized as a single Gasai control-token |
| sequence in the **`gasai`** field. |
|
|
| > Sister dataset to [`GasaiAI/Gasai-Agent-5k`](https://huggingface.co/datasets/GasaiAI/Gasai-Agent-5k) |
| > (from nvidia/Open-SWE-Traces) — **byte-identical format**, same pipeline. |
|
|
| ## What it is |
|
|
| | | | |
| |---|---| |
| | Traces | **5,205** | |
| | Unique problems (instances) | **2,704** (≤2 rollouts kept per instance) | |
| | Total tokens (StarCoder-2 tok) | **~312M** | |
| | Tokens / trace | median ~60K | |
| | File | `train.jsonl` (~1.0 GB) | |
| | Code language | **Python** | |
| | Natural language | **English** | |
|
|
| ### Format (Gasai harness) |
| ``` |
| <|bos|><|system|>{system}<|tools|>[{read,bash,edit,write}] |
| <|user|>{structured task} |
| <|assistant|><think>{dense reasoning}</think><|tool_use|>{"name","input"}<|eos|> |
| <|tool_result|>{"tool_use_id":"tc_001","name","content"}<|eos|> |
| … (tool loop) … |
| <|assistant|><think>{reasoning}</think>{final answer}<|eos|> |
| ``` |
| One `<think>` per assistant turn; `<|tool_use|>`↔`<|tool_result|>` paired by positional `tc_NNN`. |
| Schema: `{"trajectory_id": "...", "gasai": "<|bos|>…<|eos|>"}`. |
|
|
| ### Pipeline |
| 1. **Tool remap** — CoderForge's OpenHands tools (`execute_bash`, `str_replace_editor`, `finish`, `think`) |
| → the fixed Gasai 4-tool vocab `read` / `bash` / `edit` / `write`. |
| 2. **Constitution `<think>`** — the teacher's verbose reasoning re-written per turn into a dense, source-bound |
| `<think>` (every line a restated fact / grounded decision; no meta-narrative). Median ~42 tokens/think, |
| ~0.01% task-restatement. |
| 3. **Structured prompts** — first user turn restructured into a clean XML prompt (role/`<context>`, data in |
| tags, request last, embedded data in `{{ }}`). **80%** restructured; 20% keep the verbatim original |
| (fidelity fallback, never lossy). |
| 4. **Deterministic polish** — directory `read`s → `bash find`; dropped persistent-shell artifacts |
| (`C-c`/empty bash, hung "command is still running" steps); coerced object-typed edit/write args; stripped |
| emoji from finals (arrows kept); kept (incomplete) empty-final traces. |
| 5. **Dedup** — ≤2 rollouts per base instance (the source has up to 8 reward==1 rollouts per problem). |
|
|
| ## ⚠️ Known limitations (read before training) |
|
|
| This dataset was deep-audited (multi-agent semantic review). It is **pretrain-grade as one ingredient**, not a |
| clean SFT set. Honest caveats: |
|
|
| - **Benchmark contamination — NOT decontaminated.** Built from SWE-bench evaluation-harness images |
| (`sweb.eval.x86_64.*`); upstream mixes SWE-smith / SWE-rebench / R2E-Gym (the latter two are live |
| leaderboards). The 2,704 instances were **not** filtered against SWE-bench Verified/Lite or SWE-Gym test. |
| **Do not benchmark a model trained on this against those suites without first intersecting & removing |
| overlapping `instance_id`s** (it would be train-on-test). |
| - **`<think>` result-reading defect (~20–35% of traces, in-loss).** Some `<think>` blocks state "the previous |
| test failed" right after a *passing* run — the pytest `N passed` summary is buried in walls of `=` and the |
| distiller mis-read it. Affects the result-reading skill; fixable by regenerating the affected think blocks. |
| - **Oracle-test reliance (inherent).** Agents read the failing test, then implement exactly to it — valid |
| TDD, reward is honest, but the in-loss think leans on a test oracle that won't exist on real tasks, so the |
| *apparent* problem-solving capability is inflated. |
| - **Limited diversity.** ~312M tokens come from only **2,704 unique problems** (×≤2 rollouts); volume ≠ |
| problem diversity. Mix with other sources for breadth. |
| - **Minor:** ~7% of traces edit test files (a "fix the test" anti-pattern); first step is uniformly |
| `bash find /testbed -maxdepth 2`; observations carry OpenHands wrappers (`[exit code]`, `cat -n` numbering) |
| and `/testbed` paths (conditioning only — tool observations are not in loss). |
| |
| ## Source & license |
| Derived from [`togethercomputer/CoderForge-Preview`](https://huggingface.co/datasets/togethercomputer/CoderForge-Preview), |
| config `filtered_reward1` (the curated `reward==1` union of SWE-smith / SWE-rebench / R2E-Gym tasks). |
| Underlying repositories carry their own permissive licenses (MIT / Apache-2.0 / BSD; see the per-row |
| `license` in the source). Please cite CoderForge-Preview and the upstream task sets. |
| |
