Dataset Viewer
Auto-converted to Parquet Duplicate
task_id
string
rollout_index
int64
status
string
error
string
wall_time_seconds
float64
created_at
string
collection_id
string
reward
float64
num_trajectories
int64
num_steps
int64
num_events
int64
events
list
textcraft_synth.val.12
0
completed
null
5.035912
2026-03-25T10:29:52.420551+00:00
067887f6-4a76-4f95-8d3e-7d90b8d7025c
1
1
9
12
[ { "type": "trajectory_created", "ts": 1774434587.421833, "process_id": 875609, "collection_id": "067887f6-4a76-4f95-8d3e-7d90b8d7025c", "trajectory_id": "bab877d9-8056-436f-8f22-9edcf558e7bd", "step_index": null, "reward": null, "finish_message": null, "error_message": null, ...
textcraft_synth.val.17
0
completed
null
12.14221
2026-03-25T10:57:41.516100+00:00
2d615a7a-6c43-424c-a991-f529e0b257d1
1
1
17
20
[ { "type": "trajectory_created", "ts": 1774436249.4071295, "process_id": 875609, "collection_id": "2d615a7a-6c43-424c-a991-f529e0b257d1", "trajectory_id": "6803baed-2a89-48b4-a340-fe3056714916", "step_index": null, "reward": null, "finish_message": null, "error_message": null, ...
textcraft_synth.val.19
0
completed
null
3.511217
2026-03-25T08:35:24.984370+00:00
7f261017-bd3a-43af-9e4d-af3235701bfa
1
1
7
10
[ { "type": "trajectory_created", "ts": 1774427721.5132675, "process_id": 875609, "collection_id": "7f261017-bd3a-43af-9e4d-af3235701bfa", "trajectory_id": "0f587002-9cbf-4d50-a9ea-8000d5fd9e29", "step_index": null, "reward": null, "finish_message": null, "error_message": null, ...
textcraft_synth.val.23
0
completed
null
102.692845
2026-03-25T11:09:27.466335+00:00
09bb7a09-7d0e-4b1f-b2d1-7f3b0796c620
1
1
175
178
[{"type":"trajectory_created","ts":1774436864.767658,"process_id":875609,"collection_id":"09bb7a09-7(...TRUNCATED)
textcraft_synth.val.28
0
completed
null
5.39624
2026-03-25T10:57:29.338314+00:00
99f77b95-2b70-40ce-8a89-d2eba0e60f51
0
1
9
12
[{"type":"trajectory_created","ts":1774436243.9822693,"process_id":875609,"collection_id":"99f77b95-(...TRUNCATED)
textcraft_synth.val.31
0
completed
null
189.29721
2026-03-25T09:08:33.516829+00:00
4a346153-c6a7-4891-8d22-fa90793ef6e6
0
1
244
247
[{"type":"trajectory_created","ts":1774429524.2277107,"process_id":875609,"collection_id":"4a346153-(...TRUNCATED)
textcraft_synth.val.32
0
completed
null
17.135966
2026-03-25T09:31:10.857666+00:00
6657d53b-bec7-4397-919d-3397f41c63da
1
1
33
36
[{"type":"trajectory_created","ts":1774431053.7551427,"process_id":875609,"collection_id":"6657d53b-(...TRUNCATED)
textcraft_synth.val.37
0
completed
null
172.486006
2026-03-25T07:57:04.386729+00:00
33182b1a-688a-4461-946c-e624f781cebb
0
1
242
245
[{"type":"trajectory_created","ts":1774425251.9097981,"process_id":875609,"collection_id":"33182b1a-(...TRUNCATED)
textcraft_synth.val.45
0
completed
null
17.606253
2026-03-25T10:05:32.942579+00:00
482362b0-a9b7-4677-a214-d649f7f4adae
1
1
35
38
[{"type":"trajectory_created","ts":1774433115.3694298,"process_id":875609,"collection_id":"482362b0-(...TRUNCATED)
textcraft_synth.val.51
0
completed
null
238.493026
2026-03-25T09:03:54.654858+00:00
356e1ad2-a9bc-48c5-8364-3423e65ac913
0
1
318
321
[{"type":"trajectory_created","ts":1774429196.1806364,"process_id":875609,"collection_id":"356e1ad2-(...TRUNCATED)
End of preview. Expand in Data Studio

textcraft-synth-single-agent

TextCraft (synthetic) rollouts from a single (linear) Qwen3-4B agent. The agent was trained with a 40K context window; these rollouts were run without a context limit.

There is one row per rollout. The full event log for that rollout is in the events column.

import json
from datasets import load_dataset

ds = load_dataset("apurvaga/textcraft-synth-single-agent", split="train")
row = ds[0]
for e in row["events"]:
    if e["type"] == "trajectory_step_added":
        step = json.loads(e["step"])  # thought, code, output, error, reward, misc

Columns

column description
task_id, rollout_index which task and rollout this row is
status, error, wall_time_seconds, created_at from the rollout's metadata.json
collection_id ID of the trajectory collection (root agent and its sub-agents)
reward the root agent's final reward
num_trajectories number of agents in the rollout (1 plus the number of sub-agents)
num_steps, num_events step and event counts across all agents
events the ordered event log (see below)

Each entry in events has type, ts (Unix timestamp), process_id, collection_id, trajectory_id, step_index, reward, finish_message, error_message, trajectory, task and step. Fields that don't apply to an event type are null. trajectory, task, step and finish_message are JSON-encoded strings; decode them with json.loads.

event type fields set
trajectory_created trajectory (initial trajectory; parent_info is null for the root agent and set for sub-agents)
trajectory_task_set task (goal, id, max_steps, misc)
trajectory_step_added step_index, step (thought, code, output, error, reward, misc)
trajectory_finished reward, finish_message, error_message

Within a row, events interleaves events from the root agent and all of its sub-agents in time order. To get one agent's trajectory, filter by trajectory_id; each sub-agent's parent_info (in its trajectory_created event) links it to the agent that spawned it. Single-agent datasets have one trajectory per row. The task in trajectory_task_set is the state at the start of the run (for TextCraft, task.misc.initial_inventory is the true starting inventory).

Viewing with platoon's visualization tool

The platoon viewer reads JSONL event logs. hf_to_events.py (in this repo) writes each row back out as events_<task_id>_<collection_id>.jsonl, byte-for-byte identical to the original log:

pip install datasets
python hf_to_events.py apurvaga/textcraft-synth-single-agent ./events
uv run -m platoon.visualization.cli tail --dir ./events

tail loads every event at once, so you see the full trajectory trees immediately. To watch a single run play out step by step, use replay <file.jsonl> --delay 0.25 instead.

Downloads last month
66