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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
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2026-03-01T05:00:04.023151+00:00
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textcraft_synth.val.17
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2026-03-01T05:14:13.958953+00:00
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textcraft_synth.val.19
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2026-03-01T02:53:19.886800+00:00
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[ { "type": "trajectory_created", "ts": 1772333595.717941, "process_id": 3925654, "collection_id": "7f5084c8-88e8-49a6-88c3-2e81de1e31de", "trajectory_id": "c93bd5dd-7d91-42ec-9d44-7a121457014b", "step_index": null, "reward": null, "finish_message": null, "error_message": null, ...
textcraft_synth.val.23
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2026-03-01T05:23:08.680313+00:00
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textcraft_synth.val.28
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textcraft_synth.val.31
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2026-03-01T03:43:36.447317+00:00
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textcraft_synth.val.32
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textcraft_synth.val.37
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textcraft_synth.val.45
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textcraft_synth.val.51
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2026-03-01T03:00:05.039698+00:00
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End of preview. Expand in Data Studio

textcraft-synth-recursive-agent

TextCraft (synthetic) rollouts from a recursive Qwen3-4B agent (CISPO-trained, depth-aware) that can spawn sub-agents. 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-recursive-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-recursive-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.

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