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Found 12 papers, 1 papers with code
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains.
Process Supervision-Guided Policy Optimization for Code Generation
Reinforcement Learning (RL) with unit test feedback has enhanced large language models (LLMs) code generation, but relies on sparse rewards provided only after complete code evaluation, limiting learning efficiency and incremental improvements.
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
Reinforcement Learning (RL) plays a crucial role in aligning large language models (LLMs) with human preferences and improving their ability to perform complex tasks.
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals.
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward
In this paper, we consider the case where the target task is mismatched from but similar with that of the expert.
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Experimental results demonstrate that our proposed method achieves policy generalization to unseen compositional tasks in a zero-shot manner.
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important.
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
Different from existing IRL algorithms, by introducing an efficient continuous-domain trajectory sampler, the proposed algorithm can directly learn the reward functions in the continuous domain while considering the uncertainties in demonstrated trajectories from human drivers.
Learning to Describe Scenes with Programs
We are able to understand the higher-level, abstract regularities within the scene such as symmetry and repetition.
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters
We propose a novel approach for instance segmen- tation given an image of homogeneous object clus- ter (HOC).
Class Probability Estimation via Differential Geometric Regularization
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective.
