Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Belangrijkste concepten

Exploration-based Trajectory Optimization (ETO) enhances LLM agent performance through learning from exploration failures.

Samenvatting

LLMs have become integral in autonomous agent systems. ETO allows agents to learn from exploration failures, improving performance iteratively. The method involves exploration, gathering failure trajectories, and updating policies using contrastive learning methods like DPO. Experiments show ETO surpasses baselines consistently across complex tasks. ETO demonstrates efficiency and effectiveness even in scenarios lacking expert trajectories.

Statistieken

During the training phase of ETO, the batch size is 32 with a learning rate of 1e-6.
The β parameter in the DPO loss during training is set to 0.1.
ETO shows an impressive performance improvement of 22% over SFT on challenging out-of-distribution test sets.

Citaten

"Drawing inspiration from human learning, ETO capitalizes on exploration failures to enhance agent capabilities."
"Our experiments demonstrate that ETO consistently surpasses baseline performance by a large margin."
"In extreme scenarios where expert trajectories are not available, our approach still delivers impressive results."

Belangrijkste Inzichten Gedestilleerd Uit

Trial and Error

by Yifan Song,D... om arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02502.pdf

Diepere vragen

How can the incorporation of GPT-4 dynamically construct more diverse contrastive trajectory data

GPT-4 can dynamically construct more diverse contrastive trajectory data by identifying and flagging instances where the agent makes incorrect actions during an interaction. By leveraging the advanced capabilities of GPT-4 to analyze the sequence of actions taken by the agent, it can pinpoint specific steps where mistakes occur. This process allows for the generation of fine-grained contrastive trajectory pairs that capture not only overall task success or failure but also individual action-level discrepancies. Additionally, GPT-4 can provide insights into alternative action sequences that could lead to successful outcomes, enriching the dataset with a wider range of contrasting examples.

What are the potential benefits of first employing RFT before allowing agents to learn from exploration failures

The potential benefits of first employing RFT before allowing agents to learn from exploration failures lie in enhancing the agent's initial performance and stability. RFT augments expert trajectories with success trajectories generated by the base policy, providing a stronger foundation for subsequent learning phases. By incorporating additional successful interactions into training data through rejection sampling fine-tuning (RFT), agents gain exposure to a broader spectrum of effective strategies early on. This enriched dataset enables agents to start from a more robust position when transitioning to learning from exploration failures in later stages, potentially leading to faster convergence and improved generalization capabilities.

How can the policy trained by ETO be transferred and applied in a multi-task training scenario

The policy trained by ETO can be transferred and applied in a multi-task training scenario through transfer learning techniques and modular design principles. By leveraging transfer learning methodologies, such as feature extraction and fine-tuning, the policy learned from ETO on specific tasks can be adapted and extended to new tasks efficiently. The modular design approach allows for easy integration of task-specific modules while retaining core components learned through ETO training. This flexibility enables seamless adaptation of policies across various tasks without compromising performance or requiring extensive retraining efforts.