TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
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An attention-guided RL reward combined with diverse persuasion strategies produces higher attack success rates against large reasoning models than prior jailbreak methods.
SOAR is a unified DRL method using soft allocations, event-driven MDP, and heterogeneous graph transformers that cuts global makespan by 7.5% and average order completion time by 15.4% at sub-100ms latency in RMFS.
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
citing papers explorer
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TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency
TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
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Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models
An attention-guided RL reward combined with diverse persuasion strategies produces higher attack success rates against large reasoning models than prior jailbreak methods.
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SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems
SOAR is a unified DRL method using soft allocations, event-driven MDP, and heterogeneous graph transformers that cuts global makespan by 7.5% and average order completion time by 15.4% at sub-100ms latency in RMFS.
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A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.