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Download README.md from XingYing-stack/TIPS-Training-Data: direct link, hf CLI and curl.
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- Download file 1.21 kB
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https://huggingface.co/datasets/XingYing-stack/TIPS-Training-Data/resolve/main/README.md
- Command line
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hf download hf://datasets/XingYing-stack/TIPS-Training-Data/README.md
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curl -L -o README.md https://huggingface.co/datasets/XingYing-stack/TIPS-Training-Data/resolve/main/README.md
1.21 kB
metadata
license: apache-2.0
pretty_name: TIPS Training Data
configs:
- config_name: math
data_files:
- split: train
path: math/train.parquet
- config_name: agent
data_files:
- split: train
path: agent/train.parquet
TIPS Training Data
Outcome-labeled training trajectories used by TIPS (Thinking-Induced Process Supervision).
| Configuration | File | Examples |
|---|---|---|
| Math | math/train.parquet |
3,200 |
| Agent | agent/train.parquet |
2,905 |
TIPS trains a generative reward model to produce a reasoning chain, step-level labels, and an outcome label while using only outcome correctness as the reinforcement-learning reward.
Usage
from datasets import load_dataset
math_data = load_dataset("XingYing-stack/TIPS-Training-Data", "math", split="train")
agent_data = load_dataset("XingYing-stack/TIPS-Training-Data", "agent", split="train")
Data preparation and training code are available at https://github.com/RUCBM/TIPS.
Sources and Licenses
The math data is derived from SCAN-Pro, released under Apache-2.0. The agent data consists of rollout trajectories collected by us. Please also follow the terms of the original math data source.