synthlabs
Big-Math-RL-Verified
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs.
Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified.PERSONA
Dataset Card for PERSONAS (Prism Filter)
PERSONAS (Prism filter) is one of the largest datasets of synthetic preferences, with over 200k preferences over thousands of questions and 1k personas.
Details on the PERSONAS dataset can be found here paper link.
Note that you MUST also fill out the form on our site to receive access to the full dataset. The form is available here.
Dataset Details
Dataset Description
The personas dataset is a pluralistic… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/PERSONA.PERSONA_subset
Dataset Card for PERSONAS (Prism Filter)
PERSONAS (Prism filter) is one of the largest datasets of synthetic preferences, with over 200k preferences over thousands of questions and 1k personas.
Details on the PERSONAS dataset can be found here paper link
Note that this subset is 5% of the training split of PERSONAS. The full dataset is here, strictly available for academic use.
You MUST request access to the full persona dataset here.
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/PERSONA_subset.synthlabs-mlabonne-open-perfectblend
PerfectBlend Synth Reasoning
Synthetic reasoning traces generated for mlabonne/open-perfectblend. Each record contains conversations converted from ShareGPT format (from/value) to standard message format (role/content) with synthetically generated reasoning_content attached to each assistant turn.
Dataset Summary
27,265 records across 8 source datasets
37,158 reasoning turns (99.7% format compliance)
Average 1,376 chars per reasoning trace
Reasoning generated… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/synthlabs-mlabonne-open-perfectblend.gsm8k-SynthLabs-reasoning
GSM8K-SynthLabs
This dataset is a refined version of the GSM8K dataset, enriched with complex reasoning traces in the style of Pleias/SYNTH. It is designed for fine-tuning large language models to improve their reasoning capabilities using a structured, step-by-step thinking process.
Key Features
SYNTH Reasoning: Each problem contains a detailed reasoning trace generated by DeepSeek-V3.2, following the structured format (e.g., Query Parsing, Decomposition… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/gsm8k-SynthLabs-reasoning.synthlabs-llm-blender-mix-instruct-19k
LLM Blender Synth Reasoning
Synthetic reasoning traces for the LLM Blender Mix Instruct dataset, generated with Qwen3.6-27B and Qwen3.6-35B-A3B. Each record contains a general-purpose instruction with SYNTH-style reasoning and a generated answer.
Dataset Summary
19,010 records (1,490 dupes + 847 incomplete removed from 21,347 source)
19,010 reasoning turns (99.9% format compliance)
Average 1,130 chars per reasoning trace
Provider
Provider… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/synthlabs-llm-blender-mix-instruct-19k.