arpandeepk/swe-zero-free

SWE-ZERO-Free, 12M agentic coding trajectories made without an LLM

SWE-ZERO-12M showed you can generate agentic SWE data without Docker, just by sticking to shell commands that need no setup. We took that idea and asked whether you even need the model. You don't. So this is SWE-ZERO, but free.

This dataset has 12,238,610 mini-swe-agent trajectories across 119,084 real GitHub PRs. Same source and same format as SWE-ZERO. The only difference is how they get made. SWE-ZERO samples 100 rollouts per PR from a 1.7B model running on TPUs. We use a plain program instead. It reads the repo and the PR's gold patch, then writes a realistic explore-then-edit trajectory using tree-sitter, PageRank, and normal shell tools. No model, no GPU. The whole run cost about $13.

The real point is that with no model in the loop, making more of this is basically free. Want 50M, point it at more PRs. Scale stops being a budget question.

How it works

Every rollout starts from a real PR checkout in nebius/SWE-rebench-V2-PRs, the same source SWE-ZERO uses. A small phase machine drives the agent. tree-sitter builds a code graph and throws out any edit that breaks the parse. Edits only ever replay a line that actually appears in the gold patch, so nothing is invented. PageRank, seeded from the patched files, decides where the agent looks first, with a per-rollout knob so a PR's 100 rollouts don't come out identical.

Like SWE-ZERO, nothing actually runs. grep is an index lookup, cat and sed read in-memory text. So where an execution-free LLM has to guess what a command would print, our observations are computed from the real repo.

Does it hold up

We ran a blind A/B. 210 PRs, one trajectory from us and one from SWE-ZERO for each, shuffled, scored by an LLM panel on quality, grounding, and realism.

	ours	SWE-ZERO
win rate (excl. ties)	70.5%	29.5%
quality (1-5)	3.20	2.35
grounding	59	37
realism	65	33

We win mostly on grounding, our edits land on lines that really exist, and on actually reaching an edit at all. We lose about 12% on sloppy off-target block edits, which is the next thing to fix. The harness is committed if you want to rerun it.

Honest status

We have not done the training run yet. The plan is to train Qwen3-1.7B on subsets and measure SWE-bench Verified, matching SWE-ZERO's setup. Their curve on that model is 7, 9, 11 percent across 10K, 100K, 1M rollouts. That is the bar. Numbers go here once we have them.

Schema

Same as SWE-ZERO, plus a per-trajectory cost field.

field	type	notes
instance_id	string	PR id
repo	string	owner/name
messages	list	the multi-turn trajectory
trajectory_format	string	mini-swe-agent-1
exit_status	string	Submitted, incomplete, etc
duration_sec	float64	generation wall-clock time
cost_usd	float64	per-trajectory CPU and memory cost

Before you sample

We cover 119,084 of SWE-ZERO's 122,908 PRs. 117,175 have exactly 100 rollouts and only 18 fall short. The missing ~3.8k are giant repos that timed out during generation. A separate ~1,900 PRs have more than 100 rollouts from duplicate shards, so cap at 100 per instance_id when you build training subsets.

Caveats

This is mid-training data, not SFT, same as SWE-ZERO. There's a fair amount to be upfront about.

• Nothing executes. Observations are read from the real repo, but we never run tests or builds, so no trajectory contains test output, stack traces, or runtime errors. This won't teach test-driven debugging.
• The reasoning is templated. The THOUGHT text comes from a grammar, not a model. It's grounded in what the agent actually saw, but it reads flat and shows a plausible narration rather than real problem-solving.
• The generator already knows the fix. Every trajectory is built backward from the gold patch, so the investigation is a path toward a known answer, not a genuine search. We add randomness so a PR's 100 rollouts differ, but they don't get stuck the way a real agent does.
• Some PRs never reach an edit. When the gold patch can't be replayed as a safe edit, those rollouts explore and stop. Separately, about 20% stop without an edit on purpose to match the real distribution.
• Nothing is verified. No patch is checked against tests, and we have not run the downstream training bake-off yet (see status above).
• Intra-PR diversity is not measured yet. SWE-ZERO reports a MinHash similarity of 0.273 across a PR's rollouts. We have not computed ours.

Most rollouts don't end in a clean submit. If you filter to clean submissions only, you throw away most of the dataset.

Thanks

Built on SWE-ZERO's recipe and the nebius/SWE-rebench-V2-PRs source. Compute on Modal.