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Found 14 papers, 4 papers with code
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks.
Latent Embedding Adaptation for Human Preference Alignment in Diffusion Planners
This work addresses the challenge of personalizing trajectories generated in automated decision-making systems by introducing a resource-efficient approach that enables rapid adaptation to individual users' preferences.
Mastering Continual Reinforcement Learning through Fine-Grained Sparse Network Allocation and Dormant Neuron Exploration
In this paper, we introduce SSDE, a novel structure-based approach that enhances plasticity through a fine-grained allocation strategy with Structured Sparsity and Dormant-guided Exploration.
Position: Standard Benchmarks Fail -- LLM Agents Present Overlooked Risks for Financial Applications
Current financial LLM agent benchmarks are inadequate.
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning.
Off-dynamics Conditional Diffusion Planners
Offline Reinforcement Learning (RL) offers an attractive alternative to interactive data acquisition by leveraging pre-existing datasets.
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners
Deploying a safe mobile robot policy in scenarios with human pedestrians is challenging due to their unpredictable movements.
Large Language Models Know What Makes Exemplary Contexts
In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs).
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs
Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge.
LMExplainer: Grounding Knowledge and Explaining Language Models
By comparing the explanations generated by LMExplainer with those of other models, we show that our approach offers more comprehensive and clearer explanations of the reasoning process.
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem.
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs).
Sequence-level Intrinsic Exploration Model for Partially Observable Domains
First, we propose a new reasoning paradigm to infer the novelty for the partially observable states, which is built upon forward dynamics prediction.
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return.
