RLHF — PPO fine-tuning 🚧 not trained yet
Optimize an LLM with PPO against a reward model — the classic RLHF recipe.
Status — documented recipe (placeholder). A production-grade pipeline from Ropedia Academy for an advanced, GPU-heavy task. Everything below — base model, objective, dataset, config, the exact evaluation — is specified; the weights / metrics / figures land here automatically when you run the notebook on a GPU (one click below). Try the trained models live in the Ropedia demos Space.
At a glance
| Base model | An SFT LLM + a reward model (demo: GPT-2 sentiment) |
| Task | RL fine-tuning against a reward model |
| Training objective | PPO — maximize reward-model score with a KL penalty to the reference. |
| Track | LM · Language & multimodal |
| Built on | huggingface/trl (PPOTrainer) |
| Notebook | |
| Compute / storage / time | GPU required — see the Compute · storage · time table in the notebook |
Dataset
- Source: Prompt set (demo: IMDB).
Training config
GPU-scale — the notebook ships a demo profile (free Colab T4) and a full profile, with an exact Compute · storage · time table. Hyperparameters (optimizer, steps, batch, LoRA rank, …) are in the training cell.
Evaluation results
⏳ Pending — run the notebook on a GPU to fill this in. This lab reports mean reward (+ KL-to-reference) on a held-out split (see its Evaluate cell).
Inference example
No weights are published yet. After a GPU run, load the checkpoint/adapter the notebook saves (it also has a ready inference cell). Base model: An SFT LLM + a reward model (demo: GPT-2 sentiment).
How to fill this repo
- Open the notebook in Colab → Runtime → GPU → Run all (runs the real pipeline).
- Run its Publish to the Hugging Face Hub step (or
HfApi().upload_folder(...)) — the checkpoint +metrics.json+ figures replace this placeholder.
- Train / run on a GPU · [ ] upload weights · [ ] add
metrics.json· [ ] add figures · [ ] swap in the real results card
Limitations
Not yet trained — no numbers to report. The pipeline is GPU-heavy (see the compute table); on free Colab use the demo-scale settings. This is an educational, reproducible recipe, not a tuned production release.
License
Code: MIT (this repository). The base model (huggingface/trl (PPOTrainer)) and dataset are each under their own licenses — check the upstream source before redistribution.
Citation
@misc{ropedia_academy,
title = {Ropedia Academy: an interactive course on embodied & spatial AI},
author = {Ropedia Academy},
year = {2026},
howpublished = {\url{https://chaoyue0307.github.io/ropedia-academy/}}
}
Method / original work: Ouyang et al., InstructGPT, 2022; Schulman et al., PPO, 2017.
Related assets
- 🚀 Live demos: https://huggingface.co/spaces/cy0307/ropedia-demos
- 🤗 All models + collection: https://huggingface.co/cy0307
- 📚 Course & all labs: https://chaoyue0307.github.io/ropedia-academy/ · Labs tab
- 💻 Source / notebooks: github.com/ChaoYue0307/ropedia-academy
- 🔗 Relates to tracks: A · D
Documented placeholder in the Ropedia Academy collection — train it on a GPU to publish the real model. Contributions welcome on GitHub.