LegoFlow-SWE · 5,000 verified Harbor SWE tasks and two GLM-5.2 trajectory releases

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LegoFlow-SWE

5,000 verified Harbor SWE tasks mined by LegoFlow Curator, shipped in original and anti-hack prompt versions, plus two GLM-5.2 trajectory releases under OpenHands SDK and OpenCode, totaling 9,767 trajectories.

Release	Count	What it is
tasks/	5,000	Original prompts
tasks-anti-hack/	5,000	Same task IDs and task files, with anti-hack instructions appended to every prompt
GLM-5.2 · OpenHands SDK	4,753	Rolled out on tasks/ · 1,350 with reward = 1
GLM-5.2 · OpenCode	5,014	Rolled out on tasks/ · 1,430 with reward = 1

The two task directories contain the same 5,000 tasks, with different prompts. The downloadable GLM-5.2 trajectories were generated on tasks/.

Training results

A 1k-sample SFT from this pool reached 70.2 SWE-bench Verified / 48.8 SWE-bench Pro / 57.0 SWE-bench Multilingual, +6.8 / +10.6 / +5.3 over Qwen3.5-35B-A3B-Instruct under the same recipe.

Held fixed across every row: student Qwen3.5-35B-A3B-Base fine-tuned from scratch per source, ~1,000 trajectories per source, identical SFT settings, and evaluation on SWE-bench Verified (500), Pro (731), and Multilingual (300) under OpenHands SDK, no-hack / 200-turn / 256k. LegoFlow-SWE is sampled from this release; swerebenchv2 is the same Tracer / teacher / sampler on a public instance set (SWE-rebench-V2), so the gap between those two rows isolates the instance source.

Trajectory source	Teacher	Avg tokens	Turns	CoT turn %	CoT tok / turn	Verified	Pro	Multilingual
LegoFlow-SWE (this dataset)	GLM-5.2	130k	104	62	714	70.2	48.8	57.0
swerebenchv2 (LegoFlow on public instances)	GLM-5.2	69k	67	62	500	64.4	46.9	56.0
StepFun 3.5	—	51k	50	100	257	64.0	38.7	51.3
Qwen3.5-35B-A3B-Instruct	—	—	—	—	—	63.4	38.2	51.7
DeNovoSWE	DeepSeek v4-Pro	94k	70	97	309	59.8	33.7	48.3
Scale-SWE	DeepSeek v4-Pro	40k	28	99	181	59.2	36.0	50.0

Changing only the instance source is worth 5.8 points on Verified, 1.9 on Pro, and 1.0 on Multilingual.

Under a second scaffold. The same 1k recipe evaluated under OpenCode reaches 64.0 / 46.8 / 58.0 against an instruct reference of 53.0 / 37.7 / 51.0. Levels are not comparable across scaffolds, and the two runs draw on different Curator snapshots.

Evaluation safeguards

Our no-hack evaluation setting combines three controls:

• Anti-hack prompt: require independent solutions from the provided environment and prohibit looking up related issues, pull requests, patches, commits, discussions, or ready-made solutions.
• Git information removal: remove the original repository history and remote information before the agent starts. A fresh local baseline may be created for collecting the agent's patch.
• Network restrictions: disable general internet access during the agent's solving phase, allowing only the model-service connection needed for inference. These restrictions are enforced by the evaluation environment.

The downloadable tasks-anti-hack/ variant adds prompt instructions only; downloading it does not enable Git cleanup or network isolation. Those controls must be applied by the evaluation runner. The evaluation setting is separate from the settings used to generate the GLM-5.2 training trajectories.

Task statistics

Language rows use the language tags in task.toml; a task with more than one tag is counted in each matching row, so total is the unique-task total. fix.patch lines counts added and deleted lines together.

Language	Valid SWE Count	Avg fix.patch lines	Avg fix.patch hunks	Avg fix.patch files	Difficulty Score
c	889	1362.82	57.54	21.77	8.94
cpp	370	856.91	34.00	12.61	8.81
go	761	2356.86	117.96	27.18	8.89
java	725	554.10	31.26	13.28	9.01
javascript	372	535.40	29.36	11.23	8.84
python	716	710.72	38.91	12.36	8.90
rust	639	527.59	34.49	11.21	8.94
typescript	504	1530.30	54.55	22.04	8.87
total	5000	1142.67	53.13	17.29	8.91

Task directory structure

Each task is a directory under tasks/ or tasks-anti-hack/, named by source owner, repository, and issue/PR identifier. The two versions differ only in instruction.md. The layout follows the Harbor task format:

tasks/01mf02__jaq-96/
├── instruction.md                 # the task description shown to an agent
├── task.toml                      # Harbor metadata, limits, tags, and difficulty
├── environment/
│   ├── Dockerfile                 # reproducible task image
│   └── bug.patch                  # buggy-state setup patch
├── solution/
│   ├── fix.patch                  # reference repair patch
│   └── solve.sh                   # reference solution command/script
└── tests/
    ├── test.sh                    # Harbor test entrypoint
    └── ...                        # project-specific tests, layout varies by task

task.toml records [metadata] (difficulty, category, tags), [scoring] (difficulty_score, difficulty_label), and resource/time limits under [verifier], [agent], and [environment].

Trajectories

Each JSONL file under trajectories/ is one agent/model/task-version combination. Lines use PangUML v2: version, reward, meta_info, tools, and messages, where messages preserves assistant reasoning and tool interactions and tool-call arguments are JSON strings.

Both successful and failed rollouts are kept. The top-level reward is 0 or 1, mirroring meta_info.unique_info._reward. Missing verifier rewards map to 0, so reward = 0 covers both test failures and unscored runs, and reward = 1 records verifier success rather than an audit of hack-free behavior. Releases are unfiltered; the 1k training pools behind the numbers above were sampled from them.

File	Agent	Model	Task version	Trajectories	reward = 1	reward = 0
GLM-5.2 OpenHands SDK	OpenHands SDK 1.33.0	GLM-5.2	tasks/	4,753	1,350	3,403
GLM-5.2 OpenCode	OpenCode 1.18.7	GLM-5.2	tasks/	5,014	1,430	3,584

These files were previously named legoflow-selfmade-*.jsonl. Update any pinned hf_hub_download(filename=...) paths accordingly; the load_dataset configuration names are restored to trajectories-openhands-sdk and trajectories-opencode. Replace the former model-qualified allow-hack names in existing calls.

unique_info fields

Field	Description
_instance_id	Matches a task directory, according to the file's task version
_reward	Compatibility copy of top-level reward
_score	Rule-based trajectory quality scores, including composite_score
_instance_metadata	Task tags such as difficulty, category, and language tags
_agent_type	OpenCode only: main or subagent
_gen_params, _usage	OpenHands SDK only: generation settings and token usage

Usage

Run tasks with a Harbor-compatible runner by pointing it at a task directory:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Lego-X/LegoFlow-SWE",
    repo_type="dataset",
    allow_patterns=["tasks-anti-hack/01mf02__jaq-96/**"],
)

Load a trajectory release and keep the resolved rollouts:

from datasets import load_dataset

ds = load_dataset(
    "Lego-X/LegoFlow-SWE",
    name="trajectories-openhands-sdk",
    split="train",
    streaming=True,
)
resolved = ds.filter(lambda record: record["reward"] == 1)

Use name="trajectories-opencode" for the OpenCode release.

Citation

@misc{legoflow2026,
  title  = {LegoFlow: Easy and Interactive Code Data Engineering},
  author = {LegoX Team},
  year   = {2026},
  url    = {https://legox.net/blog/legoflow/}
}