nanoswe/nanoswe-trajs-261002

nanoswe SWE-smith tool-call trajectories (261002)

The training corpus of nanoswe/nanoswe-192h-261002, the 192 B200-hour record of the nanoswe SWE-bench Verified speedrun. Each row is one multi-turn agent trajectory (issue → tool-calling rollout), stored untokenized in nanoswe's tool-call chat format.

• 1,383,822 train trajectories in 168 parquet shards, deduplicated on traj_hash.
• One teacher: nemotron-3.5-lightning, acting through two tools, bash and file_editor, on SWE-smith-style task environments.
• 1,133 distinct repositories in train + validation; none is one of SWE-bench's 12 source repositories or a fork/mirror of one (see Contamination).

It replaces nanoswe/nanoswe-trajs-260812 (the corpus of the previous record) for the new record; the two corpora have different teachers, tool formats and schemas.

Splits

split	files	rows	what it is
train	train-00000.parquet … train-00167.parquet	1,383,822	the training set; rows are grouped by task instance, instances in a seeded random order
validation	val/val-00000.parquet	1,996	500 held-out task instances, up to 4 rollouts each; every rollout of these instances was removed from train

SUMMARY.json (repo root) records the train/validation build (row counts per origin and per shard, seed); val/VAL_MANIFEST.json lists the held-out instances and the rollouts kept for each.

Schema

Both splits share one schema.

column	type	description
messages	list<struct<role, content, parts>>	the trajectory; see Message format
patch	string	always empty in this release (the final diff is not stored)
model	string	teacher model; always nemotron-3.5-lightning
instance_id	string	SWE-smith task id owner__repo.<commit>.<mutation>
verified	bool	this rollout resolved its task (the teacher campaign's per-rollout grade)
origin	string	source family (see table)
traj_hash	string	md5 of `instance_id
exit_status	string	the teacher harness's exit status (Submitted, LimitsExceeded, …)
n_toolcalls	int32	number of tool calls (= number of assistant turns)

Origins

origin	split	rows	verified=True
swe-smith-v3-lightning	train	941,901	563,252
swe-smith-lightning	train	441,921	139,119
swe-smith-v3-lightning	validation	1,313	787
swe-smith-lightning	validation	683	255

Both resolved and unresolved rollouts are kept in train; use verified to select.

Message format

messages[0] is always a user turn holding the bare issue text: the teacher's system prompt / tool schema and the <pr_description> / <instructions> wrappers are stripped. Turns then alternate assistant / user:

• assistant: content is ""; parts holds an optional {"type": "text"} part (the teacher's reasoning) followed by exactly one {"type": "python"} part, the tool call {"name": "bash" | "file_editor", "arguments": <teacher's arguments, verbatim>}.
• user (after the first): the tool result as plain text, in the <returncode>…</returncode> / <output>…</output> envelope the teacher saw.

Every conversation ends on an assistant turn. nanoswe renders an assistant turn as

<|assistant_start|>{text}<|python_start|>{tool call}<|python_end|><|assistant_end|>

(<|python_start|>/<|python_end|> are nanochat's tool-call channel tokens; the name is historical) and trains only on assistant tokens. Readers other than nanoswe's dataloader should move parts into content for assistant turns: Arrow cannot type one column as "string or list", so the two are stored separately.

Provenance

• Rollouts are from a SWE-smith teacher campaign (swe-smith-lightning) plus a later SWE-smith-style campaign (swe-smith-v3-lightning), both generated by nemotron-3.5-lightning with the same tool set.
• A trajectory is cut at the first turn the format cannot represent (an injected format-error turn, zero or several tool calls in one turn, invalid tool-call JSON, an unknown tool); trajectories with no tool call are dropped. Observations are capped at 100,000 characters.
• Trajectories that read git history are kept: the generation sandbox severs git history past the task's base commit.
• Train rows are grouped by instance (all rollouts of an instance are contiguous; instance order is seeded), so a prefix of the shards is a random subset of whole instances.

Contamination

The train and validation splits follow the nanoswe rule: no trajectory may come from any of the 12 SWE-bench source repositories, forks included. Provenance is recoverable from every row's instance_id, whose prefix is the upstream owner__repo.

Train + validation span 1,133 distinct repositories. None is one of astropy/astropy, django/django, matplotlib/matplotlib, mwaskom/seaborn, pallets/flask, psf/requests, pydata/xarray, pylint-dev/pylint, pytest-dev/pytest, scikit-learn/scikit-learn, sphinx-doc/sphinx, sympy/sympy, nor a fork or mirror of one (GitHub API fork-parent check: 0 hits).

The validation split is held out at the instance level: its 500 instances have no rollout in train, but their repositories also appear in train.

Loading

from datasets import load_dataset
ds = load_dataset("nanoswe/nanoswe-trajs-261002")          # DatasetDict: train / validation
train = load_dataset("nanoswe/nanoswe-trajs-261002", split="train")

License

MIT, like the previous corpus nanoswe/nanoswe-trajs-260812. Tool observations quote code from the 1,133 upstream repositories, which remains under those projects' own licenses.