Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
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Updated
Jun 29, 2026 - Python
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
Official Implementation of VideoDPO
code for paper Query-Dependent Prompt Evaluation and Optimization with Offline Inverse Reinforcement Learning
ZYN: Zero-Shot Reward Models with Yes-No Questions
Synthetic data for fine tuning LLM
Code and data for "Timo: Towards Better Temporal Reasoning for Language Models" (COLM 2024)
GAN-style self-improvement loop for any text artifact: mutate, grade with a SEPARATE model, keep only verified wins (pairwise-judged), revert the rest. The git history is the improvement log.
distilled Self-Critique refines the outputs of a LLM with only synthetic data
RewardAnything: Generalizable Principle-Following Reward Models
Production-ready RLAIF trading system with multi-agent Claude AI that learns from market outcomes. Features 60+ indicators, foundation models, and serverless deployment.
Code for the paper "Improving Socratic Question Generation using Data Augmentation and Preference Optimization"
RankPO: Rank Preference Optimization
Reproducible, evaluation-first orchestration for LLM fine-tuning. RLAIF, LLM-as-Judge, DataFlywheel, RAG generation, adapter merging, benchmark eval, REST API.
(Stepwise controlled Understanding for Trajectories) -- “agent that learns to hunt"
RLAF: Reinforcement Learning from Agentic Feedback - A unified framework for training AI agents with multi-perspective critic ensembles
Data Preparation for Large Language Models — a curated companion to our JCST 2026 survey. Covers Pre-training, Continual Pre-training, and Post-training (SFT/RLHF/RLAIF) across collection, filtering, dedup, generation, evaluation.
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