datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Tau2-Bench-Airline-With-Code-Agents
Dataset Card for a Code Agent Version of Tau Bench 2 Airline
Dataset Summary
This dataset includes sample traces and associated metadata from multi-turn interactions between an code agent and AI assistant. The dataset is based on the Airline environment from Tau^2 Bench and contains traces from both the original version and a version made at Snorkel AI using code agents to solve the same tasks (indicator in the version field; details below).
Curated by: Snorkel AI… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/Tau2-Bench-Airline-With-Code-Agents.tau2-airline-deepseek-distill
τ²-bench airline · DeepSeek teacher trajectories
Successful multi-turn agent trajectories on τ²-bench's
airline domain, generated by running DeepSeek V4 Flash as the agent through the real τ²-bench
harness — same system prompt, same 14 tool schemas, same dialogue loop, same evaluator.
Used to behavior-clone the RL warm start
yuyu0529nya/qwen2.5-7b-tau2-airline-sft-lora,
which is the step 0 of the tau2_airline verl recipe.
Why these exist
GRPO on τ²-bench-airline… See the full description on the dataset page: https://huggingface.co/datasets/yuyu0529nya/tau2-airline-deepseek-distill.aprm-sft-thoughts-tau2-airline-policy_best-adamw30-lp0
Act-PRM SFT thoughts — tau2-bench airline
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-airline-policy_best-adamw30-lp0.aprm-sft-thoughts-tau2-airline-base_best-adamw30-lp0
Act-PRM SFT thoughts — tau2-bench airline
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-airline-base_best-adamw30-lp0.aprm-thought-generations-tau2-airline
Act-PRM Thought Generations — tau2-bench Airline
Latent thoughts inferred behind logged, action-only agent demonstrations by the
Act-PRM offline EM (Action Process Reward Models), for the tau2-bench airline
domain. For each logged (state s, action x) the EM-trained generator samples
G=4 candidate thoughts z, and each candidate is scored by the
length-penalized action likelihood of the logged action:
likelihood = p(x | s, z) # policy per-action-token… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-thought-generations-tau2-airline.