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XingYing-stack/TIPS-Training-Data

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 =… See the full description on the dataset page: https://huggingface.co/datasets/XingYing-stack/TIPS-Training-Data.

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face

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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

python

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.