Rajkummar Buyya

This survey provides a systematic taxonomy of recent research on rollout efficiency for reasoning-oriented reinforcement learning, classifying existing approaches from both mechanism and bottleneck perspectives.

The proposed framework consistently outperforms the baseline approaches in resource efficiency while maintaining high classification performance, demonstrating that elastic worker-pool orchestration provides an effective and cost-efficient solution for scalable LLM-driven text classification in cloud environments.

A double deep Q-Network-based proactive autoscaling approach (DDQN-Proactive) along with Resource Removal Strategy (RRS) that enhances decision-making by decoupling action selection from value evaluation, enabling more stable and adaptive scaling.

Late fusion in quantum machine learning achieves near-identical accuracy to full reconstruction while slashing costs and enhancing noise resilience.

FedCARE achieves up to 12.5% better predictive accuracy by enabling personalised model adaptations in federated healthcare settings without compromising data privacy.

DMTT is the only decentralized federated learning method that maintains high accuracy against adversarial attacks while ensuring Byzantine influence is effectively minimized.

PrefixShield transforms how multi-tenant LLMs manage shared resources, achieving up to an 84.87% victim cache hit ratio by addressing the admission-responsibility gap.

Achieving up to 40.3% cost savings in LLM inference through optimal prefix key-value placement could redefine resource allocation strategies in AI deployments.