kotraj — Korean Agentic Tool-Use Trajectories

11,357 synthetic multi-turn Korean conversations in which an assistant calls tools, reads their responses, and completes a user's task. Across three domains (commerce, travel, calendar) with 30 mock tools.

Every trajectory was produced by simulating three roles — a user, the assistant, and a deterministic mock tool environment — and then verified by replaying the recorded calls against that environment.

Version

This is v0.2. The previous release is tagged:

from datasets import load_dataset

ds = load_dataset("drlee1/kotraj")                    # v0.2 (current)
ds = load_dataset("drlee1/kotraj", revision="v0.1")   # v0.1 (10,684 trajectories)

v0.2 adds 2,282 trajectories from a second teacher and five provenance columns (teacher, judge_model, judge_a, judge_b, pair_id). Existing columns are unchanged, so v0.1 loaders keep working — pin revision="v0.1" if you need the exact earlier snapshot.

Composition

	count
verified	8,749
unverified	2,608
total	11,357

Teacher	count	pipeline
teacher-a	9,075	v0.1
teacher-b	2,282	v0.2

Both teachers are Upstage-family models; specific model identifiers are not disclosed. The v0.2 additions are retries: trajectories the v0.1 pipeline failed to verify, regenerated by the second teacher from the same task specifications. pair_id links a retry to the original attempt.

Domain	count		Case pattern	count
calendar	3,980		multi_turn	3,824
travel	3,811		sequential	3,627
commerce	3,566		single	1,611
			parallel	1,207
			no_call	1,088

Average 3.89 turns and 4.64 tool calls per trajectory. Mean judge score on verified trajectories: 4.89 / 5.

The two tiers

verified passed a deterministic rule gate and an LLM judge:

1. JSON Schema validation of every tool call's arguments
2. Call/response ID consistency across the conversation
3. Replay against the mock environment — re-executing the recorded calls must reproduce the same results and the same terminal state
4. All required_calls from the task specification satisfied, in the required order
5. Terminal state matches the task's success criteria
6. Judge scores ≥ 4/5 on goal completion, consistency, and naturalness

unverified trajectories are clean conversations — no malformed calls, no format contamination — that did not complete the task. They are kept because they are useful for preference learning and failure analysis, but filter them out for supervised fine-tuning unless you specifically want them.

verified = ds["train"].filter(lambda r: r["verify_status"] == "verified")

Roughly 60% of rule-gate rejections came from unmet required calls. The deterministic gate, not the judge, is what carries quality here.

Schema

Column	Type	Description
id	string	Trajectory identifier
domain	string	commerce | travel | calendar
tools	string (JSON)	Tool definitions shown to the model, OpenAI format
messages	string (JSON)	The conversation, OpenAI format (tool_calls, role: tool)
n_turns	int	User turns
n_tool_calls	int	Tool calls made
call_pattern	string	single | sequential | parallel | multi_turn | no_call
tool_env	string	mock (deterministic)
success_criteria	string (JSON)	Required calls and terminal-state conditions
verify_status	string	verified | unverified
judge_score	float	Mean of three judge axes, 1–5
teacher	string	teacher-a | teacher-b — which teacher generated it
judge_model	string	Which judge(s) scored it
judge_a	string (JSON)	Per-axis scores from the first judge
judge_b	string (JSON)	Per-axis scores from the second judge, when cross-judged
pair_id	string	Links a v0.2 retry to its v0.1 original
gen_model	string	upstage-family
pipeline_ver	string	v0.1 | v0.2
seed	int	Mock environment seed — replay is reproducible from this

messages and tools are JSON strings, not nested structs. Parse them:

import json
row = ds["train"][0]
messages = json.loads(row["messages"])
tools = json.loads(row["tools"])

Fine-tuning note

Tool call arguments are stored as JSON strings (OpenAI convention), but chat templates generally expect a mapping. Convert before calling apply_chat_template, or every sample containing a tool call will silently fail to serialize:

def to_template_messages(messages):
    out = []
    for m in messages:
        if m.get("tool_calls"):
            calls = [{**tc, "function": {**tc["function"],
                      "arguments": json.loads(tc["function"]["arguments"] or "{}")}}
                     for tc in m["tool_calls"]]
            out.append({**m, "content": m.get("content") or "", "tool_calls": calls})
        else:
            out.append({**m, "content": m.get("content") or ""})
    return out

A model fine-tuned on this dataset is available at drlee1/kotraj-qwen3.5-2B.

Limitations

• Three domains, 30 mock tools. Generalization to unseen tool schemas is untested.
• no_call is underrepresented (1,088 / 11,357 ≈ 10%). Models trained on this mix become more eager to call tools, which costs them on cases where the right answer is to decline or answer directly. We measured this regression in the companion model.
• Synthetic throughout. No human-written conversations and no human review of individual trajectories. Verification is automated: deterministic replay plus an LLM judge.
• Self-judged in part. For most trajectories the teacher and the judge belong to the same model family. The deterministic rule gate is independent of the model and does the bulk of the filtering, but the judge layer is not an independent check.
• Personas and situations are synthetic. Regional and register variation was prompted, not sampled from real users.

License

CC-BY-4.0. Generated using Upstage-family models under terms permitting derivative dataset release.

Citation

@misc{kotraj2026,
  title  = {kotraj: Korean Agentic Tool-Use Trajectories},
  author = {DONGRYEOL LEE},
  year   = {2026},
  url    = {https://huggingface.co/datasets/drlee1/kotraj}
}