Problem
Reward model training and agent supervision need large volumes of consistent, structured, and realistic data, but collecting and labeling that data manually is slow, expensive, and difficult to scale across multiple domains.
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Project
Autonomous Synthetic Data Factory for Agentic Reward Model Training
An end-to-end autonomous pipeline that uses multiple large language models to generate synthetic datasets, agent traces, and reward signals for RLHF and agentic model training.
PythonLLMsRLHFGitHub ActionsHugging FaceSQLite
Timeline
April 2026 - Present
Outcome
Built a zero-manual-intervention synthetic data factory with automated daily dataset publishing.
Outcome achieved
- Generated structured ReAct-style tasks across six domains using real-world source material.
- Automated daily publishing to Hugging Face with no manual operational step.
- Produced labeled traces with tool usage logs and failure classifications for reward model training.
Challenges faced
- Keeping data quality high while relying on LLM-generated task and label output.
- Designing a pipeline that could run repeatedly without manual cleanup or intervention.
- Creating useful reward signals and agreement metrics from automated labeling stages.
How I solved them
- Split the workflow into task generation, execution, dual labeling, validation, and upload stages.
- Used multiple LLMs for generation and judging instead of relying on a single model output path.
- Added constitutional labeling and validation steps before publishing the dataset artifacts.
Technical details
- Five-model orchestration pipeline for generation, execution, labeling, and validation.
- Real-world data ingestion from GitHub, Stack Overflow, and research papers.
- Automated dataset publishing and scheduled workflow execution through GitHub Actions.
- Structured trace generation for hallucinations, safety violations, and other failure classes.