Datasets:

mis2
/

Trajectory2gym

license: apache-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - tool-use
  - trajectories
  - supervised-fine-tuning
configs:
  - config_name: semantic_candidates
    data_files:
      - split: train
        path: data/*.jsonl.gz

Trajectory2Gym: decision-grounded candidate interactions

This release contains 34,772 complete, policy-visible candidate interactions for tool-use post-training, stored in seven compressed JSONL shards. These are semantic parents, not rendered children or a final scale-specific training view.

Source	Interactions
bfcl	17282
tau1	11411
tau2	6079

Model checkpoints

Download the five corresponding Trajectory2Gym paper checkpoints:

Scale	Model repository
1.7B	Trajectory2Gym-1.7B
4B	Trajectory2Gym-4B
8B	Trajectory2Gym-8B
14B	Trajectory2Gym-14B
30B-A3B	Trajectory2Gym-30B-A3B

Each model repository contains native tokenizers, chat templates, model cards, licenses, and release checksums. The candidate corpus below is not the rendered scale-specific training data; the 30B-A3B model uses an expanded corpus.

Record format

Records preserve native messages, tool definitions, supervised assistant-turn indices, context-turn counts, and stable source identities. The supervision mask selects assistant decisions; user/tool messages and masked failed actions can remain visible context.

Operational source paths and non-allowlisted top-level collection metadata are omitted. Dialogue content, native tool arguments, tool definitions, and supervision indices were checked against every original record, including nested dictionary ordering.

Loading

Native tool schemas and argument values are heterogeneous. Use a JSONL reader rather than assuming all records can be inferred as one flat Arrow schema.

import gzip, json
from huggingface_hub import hf_hub_download

path = hf_hub_download('mis2/Trajectory2gym', 'data/train-00000.jsonl.gz', repo_type='dataset')
with gzip.open(path, 'rt', encoding='utf-8') as f:
    for line in f:
        interaction = json.loads(line)
        # interaction['messages'], interaction['supervised_assistant_turn_indices']

Scope and provenance

The source is the frozen training-side candidate corpus. Tau1 Airline uses the disclosed seen-task construction/evaluation scope. Tau2 construction uses its official train partition. This release does not assert template-level separation, and it is not the current upstream benchmark test distribution. The 30B-A3B study uses an expanded corpus, not this common pool alone.

Source input SHA-256: a65a690aeb21a8e28c1865607dff93a10cbb1c311c5a7beacf0e58ed3eb94e23

See provenance.json and RELEASE_MANIFEST.json for the transformation and file hashes. Exported bytes differ from the historical source file. Native reset states and all execution certificates are not included, so these files are not a standalone environment replay package.

Content checks

Credential patterns, internal-host strings, and operational paths were checked. Benchmark-native simulated address literals and a generic path placeholder were retained through an exact-value-hash review bound to the source hash; see NATIVE_LITERAL_REVIEW.json. That review is not an independent human PII audit. Native fixture entities are retained to preserve observation-to-action dependencies.

License and attribution

Released under Apache-2.0 with the upstream MIT and Apache-2.0 notices preserved. See LICENSE, THIRD_PARTY_NOTICES.md, and licenses/.