Copertina

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Riemer, Matthew, Subbaraj, Gopeshh, Berseth, Glen, Rish, Irina
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models
di: Abbes, Istabrak, et al.
Pubblicazione: (2025)

Is Exploration or Optimization the Problem for Deep Reinforcement Learning?
di: Berseth, Glen
Pubblicazione: (2025)

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
di: Jain, Arnav Kumar, et al.
Pubblicazione: (2024)

GFlowNet Pretraining with Inexpensive Rewards
di: Pandey, Mohit, et al.
Pubblicazione: (2024)

Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
di: Tang, Hongyao, et al.
Pubblicazione: (2024)

Handling Delay in Real-Time Reinforcement Learning
di: Anokhin, Ivan, et al.
Pubblicazione: (2025)

SegDAC: Visual Generalization in Reinforcement Learning via Dynamic Object Tokens
di: Brown, Alexandre, et al.
Pubblicazione: (2025)

Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
di: Hugessen, Adriana, et al.
Pubblicazione: (2024)

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
di: Tang, Hongyao, et al.
Pubblicazione: (2025)

ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning
di: Castanyer, Roger Creus, et al.
Pubblicazione: (2025)

Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
di: Mohamed, Faisal, et al.
Pubblicazione: (2026)

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
di: Ostapenko, Oleksiy, et al.
Pubblicazione: (2025)

Improving Intrinsic Exploration by Creating Stationary Objectives
di: Castanyer, Roger Creus, et al.
Pubblicazione: (2023)

Align and Filter: Improving Performance in Asynchronous On-Policy RL
di: Honari, Homayoun, et al.
Pubblicazione: (2026)

Intelligent Switching for Reset-Free RL
di: Patil, Darshan, et al.
Pubblicazione: (2024)

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models
di: Vaidhya, Tejas, et al.
Pubblicazione: (2025)

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
di: Legate, Gwen, et al.
Pubblicazione: (2025)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
di: Lawson, Daniel, et al.
Pubblicazione: (2025)

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
di: Arefin, Md Rifat, et al.
Pubblicazione: (2024)

AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
di: Fu, Wei, et al.
Pubblicazione: (2025)

Beyond Sliding Windows: Learning to Manage Memory in Non-Markovian Environments
di: Tasse, Geraud Nangue, et al.
Pubblicazione: (2025)

Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent
di: Naganuma, Hiroki, et al.
Pubblicazione: (2026)

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning
di: Mircea, Andrei, et al.
Pubblicazione: (2025)

Multi-Task Reinforcement Learning Enables Parameter Scaling
di: McLean, Reginald, et al.
Pubblicazione: (2025)

Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
di: Darvishi-Bayazi, Mohammad-Javad, et al.
Pubblicazione: (2023)

Universal Reinforcement Learning in Coalgebras: Asynchronous Stochastic Computation via Conduction
di: Mahadevan, Sridhar
Pubblicazione: (2025)

Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models
di: Roger, Alexis, et al.
Pubblicazione: (2025)

Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
di: Pandey, Mohit, et al.
Pubblicazione: (2025)

Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning
di: Mustakim, Nasehatul, et al.
Pubblicazione: (2026)

LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
di: Roger, Alexis, et al.
Pubblicazione: (2026)

The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models
di: Riemer, Matthew, et al.
Pubblicazione: (2025)

FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems
di: Metelo, Frederico, et al.
Pubblicazione: (2025)

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching
di: Dong, Yanhao, et al.
Pubblicazione: (2025)

Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
di: Chen, Xiangwei, et al.
Pubblicazione: (2024)

StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel
di: Cutler, Dylan, et al.
Pubblicazione: (2025)

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training
di: Wu, Bo, et al.
Pubblicazione: (2025)

Understanding Asynchronous Inference Methods for Vision-Language-Action Models
di: Agouzoul, Ayoub
Pubblicazione: (2026)

Scale-free Adversarial Reinforcement Learning
di: Chen, Mingyu, et al.
Pubblicazione: (2024)

Deep RL With Information Constrained Policies: Generalization in Continuous Control
di: Malloy, Tailia, et al.
Pubblicazione: (2020)

Beyond Naïve Prompting: Strategies for Improved Context-aided Forecasting with LLMs
di: Ashok, Arjun, et al.
Pubblicazione: (2025)