Phoenix - Your AI AssistantReinforcement Learning From Human Feedback (RLHF) Implementation
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What you get with this Offer
I will implement a Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimisation (DPO) fine-tuning pipeline for your language model — covering preference dataset construction, reward model training or DPO data formatting, policy model fine-tuning, and alignment evaluation — producing a model that generates outputs your users demonstrably prefer over the base fine-tuned version. RLHF and DPO are the techniques behind the alignment improvements that make models like GPT-4 and Claude significantly more helpful and safer than their base counterparts; applying these techniques to your custom model raises output quality beyond what supervised fine-tuning alone achieves.
The implementation covers preference data collection protocol design (how to generate and annotate comparison pairs), reward model training (for RLHF) or preference pair formatting (for DPO), policy fine-tuning with TRL's PPO or DPO Trainer, KL divergence monitoring to prevent reward hacking, alignment evaluation on held-out preference pairs, and a technical report documenting the training approach, hyperparameters, and performance outcomes. DPO is recommended for most use cases as a more stable and compute-efficient alternative to full RLHF.
This service suits organisations building production AI assistants, content generation tools, or customer-facing LLM applications where output quality and alignment with user preferences is a primary product differentiator.
The implementation covers preference data collection protocol design (how to generate and annotate comparison pairs), reward model training (for RLHF) or preference pair formatting (for DPO), policy fine-tuning with TRL's PPO or DPO Trainer, KL divergence monitoring to prevent reward hacking, alignment evaluation on held-out preference pairs, and a technical report documenting the training approach, hyperparameters, and performance outcomes. DPO is recommended for most use cases as a more stable and compute-efficient alternative to full RLHF.
This service suits organisations building production AI assistants, content generation tools, or customer-facing LLM applications where output quality and alignment with user preferences is a primary product differentiator.
What the Freelancer needs to start the work
Please describe your model's current behaviour and the specific alignment improvements you want to achieve, share any existing preference data or annotation guidelines, confirm your base fine-tuned model and compute environment, and specify your evaluation criteria for measuring alignment improvement.