What is the primary role of a reward model in RLHF?

A reward model acts as a proxy for human preference, assigning a scalar score to LLM responses that guides reinforcement learning algorithms like PPO or GRPO during policy optimization.

Can this skill help improve AI reasoning capabilities?

Absolutely. It includes specific patterns for scoring the 'thinking' process within models, allowing the reward model to favor better internal logic and chain-of-thought steps.

Does this skill support training on consumer GPUs?

Yes, it includes patterns for 4-bit quantization and LoRA (Low-Rank Adaptation), significantly reducing VRAM requirements for training reward models.

How do I format data for reward model training?

Data should be formatted as preference pairs containing a prompt, a 'chosen' response (higher quality), and a 'rejected' response (lower quality).

Which libraries are used in this implementation?

The skill utilizes the Hugging Face Transformers ecosystem, specifically the PEFT library for efficient fine-tuning and the TRL library for the RewardTrainer.

RLHF Reward Model Training

Name: RLHF Reward Model Training
Author: atrawog

byatrawog

データサイエンスとML

Trains reward models for RLHF pipelines using preference datasets and sequence classification heads.

概要

This skill provides a specialized framework for developing reward models essential for Reinforcement Learning from Human Feedback (RLHF) workflows like PPO, GRPO, and RLOO. It guides developers through the process of preparing preference datasets, configuring the RewardTrainer from the TRL library, and implementing LoRA for memory-efficient sequence classification. A standout feature is its focus on scoring 'thinking quality,' allowing users to train models that evaluate the internal reasoning steps of LLMs, ensuring more stable and higher-quality reinforcement learning outcomes in Jupyter environments.

主な機能

Standardized RewardTrainer and RewardConfig implementation
LoRA (Low-Rank Adaptation) support for efficient SEQ_CLS training
Specialized scoring patterns for evaluating chain-of-thought reasoning
0 GitHub stars
Preference dataset preparation for chosen vs. rejected response pairs
Reward scaling and normalization techniques to prevent training instability

ユースケース

Fine-tuning models to recognize and reward high-quality internal reasoning
Building reward signals for GRPO or RLOO reinforcement learning pipelines
Creating automated evaluation models to score LLM response quality

概要

主な機能

Standardized RewardTrainer and RewardConfig implementation
LoRA (Low-Rank Adaptation) support for efficient SEQ_CLS training
Specialized scoring patterns for evaluating chain-of-thought reasoning
0 GitHub stars
Preference dataset preparation for chosen vs. rejected response pairs
Reward scaling and normalization techniques to prevent training instability

ユースケース

Fine-tuning models to recognize and reward high-quality internal reasoning
Building reward signals for GRPO or RLOO reinforcement learning pipelines
Creating automated evaluation models to score LLM response quality