---
license: mit
tags:
- RLinf
language:
- en
metrics:
- accuracy
base_model:
- RLinf/RLinf-OpenVLAOFT-LIBERO-130-Base-Lora
pipeline_tag: reinforcement-learning
model-index:
- name: RLinf-OpenVLAOFT-LIBERO-130
  results:
  - task:
      type: VLA             # Required. Example: automatic-speech-recognition
    dataset:
      type: libero_130          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: libero_130         # Required. A pretty name for the dataset. Example: Common Voice (French)
    metrics:
      - type: accuracy        # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 97.85      # Required. Example: 20.90
---

<div align="center">
  <img src="logo.svg" alt="RLinf-logo" width="500"/>
</div>


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<h1 align="center">RLinf: Reinforcement Learning Infrastructure for Agentic AI</h1>

[RLinf](https://github.com/RLinf/RLinf) is a flexible and scalable open-source infrastructure designed for post-training foundation models (LLMs, VLMs, VLAs) via reinforcement learning. The 'inf' in RLinf stands for Infrastructure, highlighting its role as a robust backbone for next-generation training. It also stands for Infinite, symbolizing the system’s support for open-ended learning, continuous generalization, and limitless possibilities in intelligence development.


<div align="center">
  <img src="overview.png" alt="RLinf-overview" width="600"/>
</div>

## Model Description
The RLinf-openvlaoft-libero series is trained on RLinf/RLinf-OpenVLAOFT-LIBERO-xxx-Base-Lora (including libero90 and libero130) and  Haozhan72/Openvla-oft-SFT-libero-xxx-traj1 (including libero10, libero-object, libero-goal and libero-spatial), using the same base models and training datasets as verl. Training with RLinf yields SOTA performance.

We use a mask to focus on valid action tokens, and compute token-level loss based on the Group Relative Policy Optimization (GRPO) advantage function, in order to enhance the model’s performance on spatial reasoning, object generalization, instruction generalization, and long-horizon tasks.


## Evaluation and Results
We trained four models using RLinf:

- [RLinf-OpenVLAOFT-GRPO-LIBERO-90](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-90) Model (based on [RLinf/RLinf-OpenVLAOFT-LIBERO-90-Base-Lora]((https://huggingface.co/RLinf/RLinf-OpenVLAOFT-LIBERO-90-Base-Lora)))
  - Recommended sampling settings:  `temperature = 1.6`, `top_p = 1.0`

- [RLinf-OpenVLAOFT-LIBERO-130](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-LIBERO-130) Model (based on [RLinf/RLinf-OpenVLAOFT-LIBERO-130-Base-Lora]((https://huggingface.co/RLinf/RLinf-OpenVLAOFT-LIBERO-130-Base-Lora)))
  - Recommended sampling settings:  `temperature = 1.6`, `top_p = 1.0`

- [RLinf-OpenVLAOFT-GRPO-LIBERO-object](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-object) Model (based on [Haozhan72/Openvla-oft-SFT-libero-object-traj1](https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero-object-traj1))
  - Recommended sampling settings:  `temperature = 1.6`, `top_p = 1.0`

- [RLinf-OpenVLAOFT-GRPO-LIBERO-spatial](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-spatial) Model (based on [Haozhan72/Openvla-oft-SFT-libero-spatial-traj1](https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero-spatial-traj1))
  - Recommended sampling settings:  `temperature = 1.6`, `top_p = 1.0`

- [RLinf-OpenVLAOFT-GRPO-LIBERO-goal](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-goal) Model (based on [Haozhan72/Openvla-oft-SFT-libero-goal-traj1]((https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero-goal-traj1)))
  - Recommended sampling settings:  `temperature = 1.6`, `top_p = 1.0`

- [RLinf-OpenVLAOFT-GRPO-LIBERO-long](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-long) Model (based on [Haozhan72/Openvla-oft-SFT-libero10-traj1]((https://huggingface.co/Haozhan72/Openvla-oft-SFT-libero10-traj1)))
  - Recommended sampling settings:  `temperature = 1.6`, `top_p = 1.0`


### Benchmark Results

Sft models for LIBERO-90 and LIBERO-130 are trained by ourself following training reciepe from [OpenVLA-OFT](https://github.com/moojink/openvla-oft/blob/main/vla-scripts/finetune.py). And other sft models are from [SimpleVLA-RL](https://huggingface.co/collections/Haozhan72/simplevla-rl-6833311430cd9df52aeb1f86). 
  > We evaluate each model according to its training configuration. Using libero_seed = 0 and evaluating 500 episodes for the Object, Spatial, Goal, and Long suites, 4,500 episodes for LIBERO-90, and 6,500 episodes for LIBERO-130. 
  > For the SFT-trained (LoRA-base) models, we set do_sample = False.
  > For the RL-trained models, we set do_sample = True, temperature = 1.6, and enable rollout_epoch=2, and the final results are reported as the average across the two runs.

| Model              | Object | Spatial | Goal  | Long  |   90    | Average |
| ------------------ | ------ | ------- | ----- | ----- | ------- |-------  |
| sft models         | 28.83  |  52.22  | 49.40 | 14.92 |  79.28  | 66.07   |
| trained with RLinf | [97.68](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-object)  |  [94.76](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-spatial)  | [93.96](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-goal) | [90.93](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-long) |  [96.44](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-GRPO-LIBERO-90)  | 95.79   |

Besides, we train [one model](https://huggingface.co/RLinf/RLinf-OpenVLAOFT-LIBERO-130) (we named it libero-130 model) for all tasks in libero.

| libero-130 model   | Object | Spatial | Goal  | Long  |   90    | 130(all) |
| ------------------ | ------ | ------- | ----- | ----- | ------- |-------  |
| sft models         | 50.20  | 51.61   | 49.40 | 11.90 |  42.67  | 42.09   | 
| trained with RLinf | 99.60  | 98.69   | 98.09 | 93.45 |  98.02  | 97.85   | 

<div align="center">
  <img src="tensorboard-success_once.png" alt="RLinf-libero-result" width="600"/>
</div>

## How to Use
Please integrate the provided model with the [RLinf](https://github.com/RLinf/RLinf) codebase. To do so, modify the following parameters in the configuration file ``examples/embodiment/config/libero_10_grpo_openvlaoft.yaml``:

- Set ``rollout.model.model_path``,  ``actor.model.model_path``, and ``actor.tokenizer.tokenizer_model`` to the path of the model checkpoint.

Note: If you intend to evaluate the model directly, make sure to set ``actor.model.is_lora`` to ``false``.

## License
This code repository and the model weights are licensed under the MIT License.