SciWorld-MPO

This model is a fine-tuned version of Llama-3.1-8B-Instruct on the sciworld-metaplan-preference-pairs 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.

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 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

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SciWorld-MPO