---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: reinforcement-learning
datasets:
- xwm/Meta_Plan_Optimization
base_model:
- meta-llama/Llama-3.1-8B-Instruct
metrics:
- accuracy
tags:
- nlp
- agent
---

# SciWorld-MPO

This model is a fine-tuned version of Llama-3.1-8B-Instruct on the [sciworld-metaplan-preference-pairs](https://huggingface.co/datasets/xwm/Meta_Plan_Optimization/blob/main/sciworld_metaplan_preference_pairs.json) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5017
- Rewards/chosen: -3.8774
- Rewards/rejected: -5.1594
- Rewards/accuracies: 0.6419
- Rewards/margins: 1.2820
- Logps/chosen: -92.4593
- Logps/rejected: -109.6343
- Logits/chosen: 0.5212
- Logits/rejected: 0.5151

See the original paper for more details: [MPO: Boosting LLM Agents with Meta Plan Optimization](https://hf.co/papers/2503.02682).

Code: https://github.com/WeiminXiong/MPO

## Model description

This model uses Meta Plan Optimization (MPO) to improve the planning capabilities of LLM agents. It leverages high-level general guidance through meta plans and enables continuous optimization based on feedback from the agent's task execution.  It achieves state-of-the-art performance on ALFWorld and SciWorld, with an average accuracy of 83.1.

## Intended uses & limitations

More information needed

## Training and evaluation data

The model was trained on the `sciworld-metaplan-preference-pairs` dataset, part of the [Meta_Plan_Optimization](https://huggingface.co/datasets/xwm/Meta_Plan_Optimization) dataset.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3