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Found 5 papers, 2 papers with code
LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models
Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance.
Think Before You Act: Decision Transformers with Internal Working Memory
We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training.
Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs.
Learning Multi-Objective Curricula for Robotic Policy Learning
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).
MOBA: Multi-teacher Model Based Reinforcement Learning
Results show that, compared with SOTA model-free methods, our method can improve the data efficiency and system performance by up to 75% and 10%, respectively.
