rohanjain2312/toolsmith-tasks

ToolSmith Tasks

Synthetic travel-ops tool-calling tasks for training and evaluating Qwen3-4B-Instruct-2507 (via LoRA SFT + step-level GRPO), generated against the ToolSmith deterministic 12-tool sandbox.

Dataset Summary

• Tasks: 594 across 4 difficulty tiers (T1 single-tool, T2 2-tool chains, T3 4-6 tool chains with dependencies, T4 traps — unsolvable/ambiguous requests where the correct behavior is to decline or ask for clarification, not hallucinate a tool call)
• Splits: train / val / test, stratified by tier (417 / 89 / 88)
• Generation: synthetic, produced by a local template-based generator (scripts/generate_tasks_local.py) parameterized across the sandbox's real world data (src/toolsmith/tools/sandbox/worlddata/) — an alternative to prompting Claude directly (scripts/generate_tasks.py) for environments without a live Anthropic API key. Every multi-step task's dependent values (e.g. a city's lat/lon before a weather lookup) are computed by actually executing the relevant sandbox tool, not invented. Validated for 100% solvability by a bounded BFS solver (src/toolsmith/data/solver.py); T4 traps are trivially "solvable" with zero tool calls by design (the correct answer never needs one).
• Goal specs: every task carries a machine-checkable goal spec (not a gold trajectory) — rewards verify sandbox outcomes, not paths

Fields

Field	Type	Description
id	string	unique task id
tier	string	one of T1, T2, T3, T4
user_prompt	string	the natural-language traveler request
goal_spec	list	machine-checkable conditions (see below)
min_steps	int	solver-computed minimum sandbox tool calls to satisfy the goal
split	string	one of train, val, test

Goal condition types

• answer_contains_fact — final answer text must contain a given substring
• tool_was_called_with — a successful tool call must match a tool name + arg subset
• calendar_event_exists — a calendar_create_event call with exact fields must have succeeded
• numeric_within_tolerance — a number from the final answer or a tool result must be near an expected value

How This Dataset Is Used

• SFT (notebooks/src/01_sft_warmstart.py): train-split tasks are replayed through an agent inside the episode runner; trajectories whose goal spec passes become gold SFT rows (scripts/generate_gold_trajectories.py, or its local equivalent scripts/generate_gold_trajectories_local.py, which scripts the exact required tool-call sequence from each goal_spec directly rather than improvising it, since every T1-T3 goal_spec already IS that sequence).
• GRPO (notebooks/src/02_grpo_training.py): goal_spec feeds the R5 outcome reward (src/toolsmith/rewards/outcome_reward.py) directly — every candidate action is scored by executing it in the sandbox and checking these same conditions. min_steps feeds the R6 efficiency bonus.
• Eval (src/toolsmith/eval/runner.py): the test split is the held-out 4-way comparison suite (88 tasks); see the model card's evaluation table for results.

Contamination Controls

SFT gold trajectories are drawn only from the train split; test never touches training. This dataset was generated independently of, and shares no rows with, the xLAM/Glaive public function-calling corpora used for SFT warm-start.

License

MIT.