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Palm Rlhf Pytorch

Palm Rlhf Pytorch is a work-in-progress Python/PyTorch library implementing reinforcement learning from human feedback for the PaLM architecture.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Palm Rlhf Pytorch — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to study or adapt a PaLM-oriented RLHF training implementation when you can supply the data, model weights and compute needed for training.

Who it is for

  • Developers and researchers exploring human-feedback-based fine-tuning workflows for PaLM-style models.
  • Learners examining how language-model training, reward modeling and policy optimization fit together.

Documented uses

  • Train a PaLM model and generate sequences.
  • Train a reward model using curated human feedback and prompt/response masks.
  • Provide a PaLM model, reward model and prompt token IDs to an RLHF trainer.
Before you choose

Requirements & limits

The public repository reports an MIT license. The README provides a pip installation command; it does not include a trained model for inference.

  • The project is explicitly work in progress, and its README lists additional testing and development as todo items.
  • Its examples require PyTorch and use CUDA; Flash Attention is shown as an option.
  • The complete workflow depends on curated human-feedback data, prompt data, pretrained weights for the trainer stage, and substantial training resources.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train a PaLM model and generate sequences

Run the README's example of autoregressive PaLM training and sequence generation.

Before you start

  • Install the package with pip install palm-rlhf-pytorch.
  • Use PyTorch and a CUDA device, as shown in the example.

Steps

  1. Import torch and PaLM from palm_rlhf_pytorch.

  2. Create a PaLM instance with num_tokens=20000, dim=512, depth=12, and flash_attn=True, then move it to CUDA.

  3. Create a CUDA token sequence with torch.randint(0, 20000, (1, 2048)), calculate palm(seq, return_loss=True), and call loss.backward().

  4. After training, call palm.generate(2048) to generate a sequence.

Expected outcome

The example computes a training loss and shows sequence generation after training.

Guide limitations

  • The project is marked work in progress.
  • The repository does not include a trained model for inference; the README describes generation after substantial training.
Documentation 1 ↗
Source-based practical guide

Train a reward model from curated human feedback

Use the README's example to train a binned-output reward model and obtain a reward score.

Before you start

  • Install the package with pip install palm-rlhf-pytorch.
  • Have curated human-feedback data; the README's code uses mock tensors as an illustration.
  • Use PyTorch and a CUDA device, as shown in the example.

Steps

  1. Import torch, PaLM, and RewardModel from palm_rlhf_pytorch.

  2. Create a PaLM with num_tokens=20000, dim=512, depth=12, and causal=False; wrap it in RewardModel with num_binned_output=5 and move the reward model to CUDA.

  3. Prepare token sequences, a boolean prompt_mask distinguishing prompt from response, and rating labels.

  4. Compute reward_model(seq, prompt_mask=prompt_mask, labels=labels) and call loss.backward() to train.

  5. After training, call reward_model(seq, prompt_mask=prompt_mask) to obtain a reward.

Expected outcome

The example computes a reward-model training loss and shows how to obtain a reward after training.

Guide limitations

  • The README notes that fine-tuning a reward model from a pretrained transformer had overfitting issues in the original paper; LoRA is presented as an open-research option.
  • The repository does not provide a trained model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
lucidrains/PaLM-rlhf-pytorch · Official homepage title · retained in its source language
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