while-ai
Datasets
All datasets matching “while-ai”agent-simulations
Agent Simulations
Made with the whileai SDK · Collections: Simulation, Start here: foundational post-training datasets
53,971 synthetic agent trajectories generated by simulations
across 34 agent types. The rows include successful and failed
trajectories for supervised fine-tuning, preference work, reinforcement learning, and
evaluation.
NOTE: This is generated test and training data, not curated ground truth. Review and
filter it for your application before training or… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/agent-simulations.text-to-sql-shop
Text-to-SQL on a seeded store schema, with checkpoints
Recipe: recipes/04-train/text-to-sql · Collection: Analyst
A question about an online store's database in, one PostgreSQL query out, graded by
a program: run the query, compare the result set to the gold query's result. The
schema (8 tables, seeded, schema.sql + seed.sql), the verifier, the trainer and
the benchmark runner are the
recipes/04-train/text-to-sql
recipe in the open-source whileai SDK.
Splits
|… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/text-to-sql-shop.identity-behavior
identity-behavior
Recipe: recipes/04-train/identity · Collections: Character, Start here: foundational post-training datasets
Teach an open model who it is.
Identity behavior is the simplest thing every shipped assistant needs and
open models do not have out of the box: a consistent answer to "who are
you?" and "who made you?", in every phrasing and every language, without a
system prompt propping it up. Ask a base Qwen model and it tells you about
Alibaba; put a persona in the… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/identity-behavior.tool-call-efficiency
tool-call-efficiency
Made with the whileai SDK · Collections: Efficiency, Start here: foundational post-training datasets
Teach an agent to make every tool call count.
An agent that calls a tool twice with the same arguments, looks up what
the user just told it, or keeps calling after the task is done is slow,
expensive, and harder to trust. Ask a base Qwen3-4B to work through
1,133 tool-using tasks across six agents and it does this a lot:
only 52% of its 6,681 rollouts finish… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/tool-call-efficiency.tau2-simulated
tau2 Simulated Training Set
Made with the whileai SDK · Collections: Simulation, Start here: foundational post-training datasets
The training set that took a base model from 5% to 30% on tau2-bench
telecom, made from nothing but the agent's tool list and policy.
If you build a customer-facing agent, you already have the two files this
dataset was made from: the tools it can call and the policy it follows.
The whileai SDK turned those into 1,057 graded conversations across the… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/tau2-simulated.retail-voice-concise
retail-voice-concise
Made with the whileai SDK · Collection: Register
The same speaking register as
airline-voice-concise,
trained on a different agent. A retail support agent that leads with the
answer and stops.
This exists to test the limitation stated on the airline card: that nothing
there showed the register transfers off airline content. It does. Same
constitution, same recipe, different world, different tools, different
records.
Trained on this set, Qwen3-4B goes from… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/retail-voice-concise.