What is RLOO in the context of LLM training?

RLOO (Reinforcement Learning with Leave-One-Out) is a variance reduction technique that stabilizes training by comparing a specific completion's reward against the average of other generated completions for the same prompt.

Can I use this skill to train reasoning models?

Yes, this skill includes specialized patterns for 'thinking' models, including token-based reward functions that specifically detect and score the reasoning content before the final answer.

Does this skill support LoRA/QLoRA?

Yes, the implementation patterns include LoRA application via FastLanguageModel, allowing for efficient parameter updates without needing to fine-tune the entire model.

How does RLOO differ from GRPO?

While both use multiple generations, RLOO uses a leave-one-out mean for the baseline calculation, which often results in lower variance and more stable policy updates compared to GRPO's group mean.

What are the memory requirements for RLOO?

RLOO requires enough VRAM to handle multiple completions per prompt; however, this skill utilizes Unsloth's 4-bit quantization and gradient checkpointing to make training accessible on mid-to-high-end consumer GPUs.

RLOO Policy Optimization

Name: RLOO Policy Optimization
Author: atrawog

byatrawog

データサイエンスとML

Implements Reinforcement Learning with Leave-One-Out estimation to stabilize model training and optimize policy performance.

概要

RLOO is a specialized reinforcement learning skill for Claude Code that focuses on variance reduction during policy optimization. By using a leave-one-out baseline estimation—where multiple completions are generated per prompt and compared against each other—it provides significantly more stable gradients than traditional single-sample RL methods. This skill is particularly effective for training reasoning-capable models (such as Qwen-Thinking) and includes pre-defined patterns for reward function integration, thinking-aware token boundaries, and memory-efficient training configurations using Unsloth and TRL.

主な機能

Optimized hyperparameters for training reasoning and chain-of-thought models
Thinking-aware reward functions with token-based boundary detection
Leave-One-Out baseline estimation for superior variance reduction
Seamless integration with Unsloth for memory-efficient 4-bit and bf16 training
Standardized RLOOTrainer patterns for stable policy optimization
0 GitHub stars

ユースケース

Training LLMs to follow complex logical reasoning and chain-of-thought patterns
Stabilizing RL training sessions where standard methods like GRPO exhibit high variance
Optimizing model behavior through multi-completion reward-based scoring

概要

主な機能

Optimized hyperparameters for training reasoning and chain-of-thought models
Thinking-aware reward functions with token-based boundary detection
Leave-One-Out baseline estimation for superior variance reduction
Seamless integration with Unsloth for memory-efficient 4-bit and bf16 training
Standardized RLOOTrainer patterns for stable policy optimization
0 GitHub stars

ユースケース

Training LLMs to follow complex logical reasoning and chain-of-thought patterns
Stabilizing RL training sessions where standard methods like GRPO exhibit high variance
Optimizing model behavior through multi-completion reward-based scoring