mlsanityData to 14 Oct 2026

PithTrain: A Compact and Agent-Native MoE Training System

arXiv 2605.31463 ↗First posted 29 May 2026cs.LG cs.AI cs.CLcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

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Abstract (arXiv)

Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built optimized MoE training stacks over years of engineering effort. Yet evolving these stacks for new architectures and system optimizations remains expensive. With the rise of AI coding agents, they could automate parts of training-framework development and accelerate this evolution. But applying them to these existing frameworks carries hidden costs, invisible to today's throughput-only evaluations. We name this missing dimension agent-task efficiency (ATE): the cost of using coding agents to understand, operate, and extend a framework. Grounded in four agent-native design principles, we build PithTrain, a compact, agent-native MoE training framework. We further introduce ATE-Bench, covering real-world training-framework tasks. Our evaluation shows PithTrain matches the throughput of production frameworks, and on ATE-Bench, PithTrain enables higher agent-task efficiency, with up to 62% fewer Agent Turns and 64% less Active GPU Time.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 26 of its 39 references that are in the map. First posted on arXiv 29 May 2026.

Builds on

26 of 39 references.

References · 26

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  5. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  6. 2021Program Synthesis with Large Language Modelscited by 3,637
  7. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
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  9. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,164
  10. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
  11. 2020GLU Variants Improve Transformercited by 1,804
  12. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
14 more
  1. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,631
  2. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,301
  3. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 660
  4. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
  5. 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineeringcited by 469
  6. 2023Ring Attention with Blockwise Transformers for Near-Infinite Contextcited by 457
  7. 2024DataComp-LM: In search of the next generation of training sets for language modelscited by 398
  8. 2025KernelBench: Can LLMs Write Efficient GPU Kernels?cited by 225
  9. 2025MoBA: Mixture of Block Attention for Long-Context LLMscited by 198
  10. 2024TorchTitan: One-stop PyTorch native solution for production ready LLM pre-trainingcited by 85
  11. 2024Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Modelscited by 56
  12. 2024MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Expertscited by 56
  13. 2025Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Modelscited by 54
  14. 2026FlashInfer-Bench: Building the Virtuous Cycle for AI-driven LLM Systemscited by 25

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