Skill UI

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.

High-performance RLHF framework that combines Ray, vLLM, and ZeRO-3 to accelerate PPO/GRPO/RLOO/DPO training for 7B-70B+ models on distributed GPU clusters, providing a single-stack workflow for reward models and policy optimization.

Guides training large language models with Volcano Engine's verl library, covering PPO/GRPO workflows, distributed backends, multi-turn rollouts, reward configs, critic-enabled PPO, and production deployments using Ray workers.

This framework outlines the 'Hook Model' (Trigger, Action, Variable Reward, Investment) for building deeply engaging, habit-forming product loops. It guides designers in optimizing user retention, understanding behavioral psychology, and structuring product features to move users from deliberate usage to automatic, routine behavior. Use it for onboarding, feature design, and improving long-term user stickiness.

Provides expert guidance on building, optimizing, and analyzing growth-driving programs. This skill covers the fundamental differences between customer referral schemes and paid affiliate models. Learn to identify key trigger moments, structure compelling incentive mechanisms (single/double-sided), and calculate optimal reward sizes to maximize viral loops and scale customer acquisition for sustained business growth.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.