#scheduling-algorithms News & Analysis

2 articles tagged with #scheduling-algorithms. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 116/10

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Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

Researchers introduce CDQAC, an offline reinforcement learning algorithm that learns effective job scheduling policies from static, suboptimal datasets rather than requiring extensive online training interactions. The breakthrough demonstrates that scheduling performance depends primarily on state-action coverage rather than trajectory quality, enabling the algorithm to learn effectively from even simple random heuristics while requiring only 1-5% of original dataset size.

AIBearisharXiv – CS AI · May 286/10

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DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents

Researchers introduce DynaSchedBench, a calibrated framework for testing AI agents on dynamic job scheduling problems, revealing that large language models underperform expectations. The study uncovers an 'Observability Paradox' where providing agents with complete information actually degrades performance, and shows LLM-based schedulers fail to consistently outperform traditional heuristic baselines despite significant computational overhead.