constraints
IF_multi_constraints_upto5
Dataset
This is the IF-RLVR training data, with up to 5 constraints per instruction. The constraints are sampled from IFEval (25) and IFBench-Train (29).
License
This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines. This dataset includes output data generated from third party models that are subject to separate terms governing their use.
Citation
Please cite:… See the full description on the dataset page: https://huggingface.co/datasets/allenai/IF_multi_constraints_upto5.RLVR-GSM-MATH-IF-Mixed-Constraints
GSM/MATH/IF Data - RLVR Formatted
Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data.
This dataset contains data formatted for use with open-instruct - specifically reinforcement learning with verifiable rewards.
It was used to train the final Tulu 3 models with RL, and contains the following subsets:
GSM8k (7,473 samples): The GSM8k train set formatted for use with RLVR and open-instruct. MIT License.
MATH (7,500… See the full description on the dataset page: https://huggingface.co/datasets/allenai/RLVR-GSM-MATH-IF-Mixed-Constraints.starling-transfer-shared-eval-no-constraints-no-source-value-train-2p5pct-stratifiedstarling-transfer-shared-eval-no-constraints-source-value-train-2p5pct-stratifiedagi-biospheric-10-constraints
AGI Biospheric — 10 Core Biospheric Constraints and 52 Interdependencies
Overview
AGI Biospheric is a bilingual (French / English) structured dataset modeling 10 core biospheric constraints and 52 direct interdependencies relevant to long-term reflection on AGI alignment, ecological limits, civilizational resilience, and the material conditions of intelligence.
This repository is the first public machine-readable release of the AGI Biospheric framework. It should… See the full description on the dataset page: https://huggingface.co/datasets/Cedre83/agi-biospheric-10-constraints.IF_multi_constraints_upto5_SFT
Description
This dataset was derived from the allenai/IF_multi_constraints_upto5 constrained instruction-following dataset, adapting it for supervised fine-tuning. Responses were generated with google/gemma-4-31B-it and then filtered to retain only high-quality examples. The final dataset includes only samples with a minimum loose score of 1.0 and a minimum strict score of 0.8 according to the official IFBench evaluator. Maximum response length was limited to 1024 tokens, all… See the full description on the dataset page: https://huggingface.co/datasets/UniLu/IF_multi_constraints_upto5_SFT.