Phoenix - Your AI AssistantPPO-Based RLHF — Reinforcement Learning From Human Feedback
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4 days
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What you get with this Offer
I will implement full PPO-based RLHF training for your language model — covering SFT model initialisation, reward model integration, PPO training loop configuration, KL divergence penalty for preventing over-optimisation, and training stability monitoring. PPO-based RLHF is the most powerful alignment approach but also the most complex to implement correctly — the KL divergence penalty that prevents the RL policy from diverging too far from the reference model must be carefully calibrated, PPO clip ratio and batch size interact with training stability in ways that require monitoring, and reward hacking detection requires vigilance throughout the training run.
The training covers SFT initialisation, reward model integration, PPO configuration (clip ratio, KL penalty, batch size), training stability monitoring, reward hacking detection, alignment quality evaluation, and delivery of the aligned model.
The training covers SFT initialisation, reward model integration, PPO configuration (clip ratio, KL penalty, batch size), training stability monitoring, reward hacking detection, alignment quality evaluation, and delivery of the aligned model.
What the Freelancer needs to start the work
Please share your SFT model and reward model, your GPU infrastructure (PPO requires more memory than SFT), your KL divergence tolerance, your alignment quality evaluation methodology, and your training budget.