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Production-grade RLHF & post-training pipeline built from first principles in PyTorch. Features causal transformers, LoRA SFT, Bradley-Terry reward modeling, DPO, IPO, PPO with GAE, DeepSeek-R1 style GRPO reasoning, and automated statistical release gating.
RLHF is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 27/100. Production-grade RLHF & post-training pipeline built from first principles in PyTorch. Features causal transformers, LoRA SFT, Bradley-Terry reward modeling, DP. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding RLHF to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/jayeshpandey01/RLHF.gitReview the upstream source before running this command.
Downloads a script and pipes it straight into a shell, executing remote code with no review.
curl -LsSf https://astral.sh/uv/install.sh | shDispute this flag?Executes inline Python code, which can hide malicious behavior in a compact payload.
python -c "Dispute this flag?Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "jayeshpandey01/RLHF"
}Production-grade RLHF & post-training pipeline built from first principles in PyTorch. Features causal transformers, LoRA SFT, Bradley-Terry reward modeling, DPO, IPO, PPO with GAE, DeepSeek-R1 style GRPO reasoning, and automated statistical release gating.