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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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pipeline_tag: reinforcement-learning |
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datasets: |
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- xwm/Meta_Plan_Optimization |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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metrics: |
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- accuracy |
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tags: |
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- nlp |
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- agent |
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--- |
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# SciWorld-MPO |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5017 |
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- Rewards/chosen: -3.8774 |
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- Rewards/rejected: -5.1594 |
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- Rewards/accuracies: 0.6419 |
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- Rewards/margins: 1.2820 |
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- Logps/chosen: -92.4593 |
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- Logps/rejected: -109.6343 |
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- Logits/chosen: 0.5212 |
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- Logits/rejected: 0.5151 |
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See the original paper for more details: [MPO: Boosting LLM Agents with Meta Plan Optimization](https://hf.co/papers/2503.02682). |
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Code: https://github.com/WeiminXiong/MPO |
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## Model description |
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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. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 4 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
