# Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling

arXiv: https://arxiv.org/abs/2506.02025 · HTML: https://mlsanity.com/paper/2506.02025/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 29 May 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 11 of 11)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Efficient LLM Scheduling by Learning to Rank](https://mlsanity.com/paper/2408.15792/) (2024)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints](https://mlsanity.com/paper/2410.06458/) (2024)
- [A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1](https://mlsanity.com/paper/2502.10867/) (2025)
- [The Landscape and Challenges of HPC Research and LLMs](https://mlsanity.com/paper/2402.02018/) (2024)
- [Self-Resource Allocation in Multi-Agent LLM Systems](https://mlsanity.com/paper/2504.02051/) (2025)
- [MRSch: Multi-Resource Scheduling for HPC](https://mlsanity.com/paper/2403.16298/) (2024)
- [LLMSched: Uncertainty-Aware Workload Scheduling for Compound LLM Applications](https://mlsanity.com/paper/2504.03444/) (2025)
- [Scalable HPC Job Scheduling and Resource Management in SST](https://mlsanity.com/paper/2501.18191/) (2025)

## Built on by (most cited first, 3 of 3)

- [Evaluating Large Language Models for Workload Mapping and Scheduling in Heterogeneous HPC Systems](https://mlsanity.com/paper/2511.11612/) (2025)
- [PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems](https://mlsanity.com/paper/2607.26710/) (2026)
- [When Do LLM Agents Help? Deadline-Aware Mixed-Criticality Task Scheduling at the Autonomous-Vehicle Edge](https://mlsanity.com/paper/2608.19557/) (2026)

## Abstract (arXiv)

High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based, e.g., First-Come-First-Served (FJFS) and Shortest Job First (SJF), or intensive optimization techniques, often lack adaptability to dynamic workloads and, more importantly, cannot simultaneously optimize multiple objectives in HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason + Act), enabling iterative, interpretable decision-making. The system incorporates a scratchpad memory to track scheduling history and refine decisions via natural language feedback, while a constraint enforcement module ensures feasibility and safety. We evaluate our approach using OpenAI's O4-Mini and Anthropic's Claude 3.7 across seven real-world HPC workload scenarios, including heterogeneous mixes, bursty patterns, and adversarial cases etc. Comparisons against FCFS, SJF, and Google OR-Tools (on 10 to 100 jobs) reveal that LLM-based scheduling effectively balances multiple objectives while offering transparent reasoning through natural language traces. The method excels in constraint satisfaction and adapts to diverse workloads without domain-specific training. However, a trade-off between reasoning quality and computational overhead challenges real-time deployment. This work presents the first comprehensive study of reasoning-capable LLMs for HPC scheduling, demonstrating their potential to handle multiobjective optimization while highlighting limitations in computational efficiency. The findings provide insights into leveraging advanced language models for complex scheduling problems in dynamic HPC environments.