PithTrain: A Compact and Agent-Native MoE Training System

• URL: http://arxiv.org/abs/2605.31463v1
• Date: Fri, 29 May 2026 15:52:58 GMT
• Title: PithTrain: A Compact and Agent-Native MoE Training System
• Abstract summary: We build PithTrain, a compact, agent-native MoE training framework.<n>On ATE-Bench, PithTrain enables higher agent-task efficiency, with up to 62% fewer Agent Turns and 64% less Active GPU Time.
• Score: 27.857461550954337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ForgeTrain: Forging Production-Grade Training Frameworks via Harness-Driven AI Development [31.93593134525649]
  Training large models still relies on general-purpose frameworks such as Megatron-LM.<n>We propose Forge Engineering: building a dedicated implementation from scratch for each scenario.<n>We instantiate this paradigm for training frameworks as ForgeTrain, which holds a trusted framework as a golden reference.
  arXiv  Detail & Related papers  (2026-09-12T01:47:57Z)
• ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search [54.43696074323815]
  ZGCM-1 is a fully open 7B foundation model trained from scratch with extreme data, system, and algorithmic efficiency.<n>We establish an AI-native R&D workflow where agent swarms autonomously manage cluster operations.<n>ZGCM-1-7B is competitive across 7B model family on general benchmarks.
  arXiv  Detail & Related papers  (2026-09-11T17:18:04Z)
• SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning [3.1436750864792375]
  We introduce SimuAgent, an LLM-powered modeling and simulation agent tailored for Simulink.<n>SimuAgent replaces XML with a concise, dictionary-style Python representation, dramatically cutting token counts.<n>A lightweight plan-execute architecture, trained in two stages, equips the agent with both low-level tool skills and high-level design reasoning.
  arXiv  Detail & Related papers  (2026-01-08T18:10:35Z)
• Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models [78.73992315826035]
  We introduce Youtu-LLM, a lightweight language model that harmonizes high computational efficiency with native agentic intelligence.<n>Youtu-LLM is pre-trained from scratch to systematically cultivate reasoning and planning capabilities.
  arXiv  Detail & Related papers  (2025-12-31T04:25:11Z)
• DiRL: An Efficient Post-Training Framework for Diffusion Language Models [54.405206032785706]
  Diffusion Language Models (dLLMs) have emerged as promising alternatives to Auto-Regressive (AR) models.<n>Existing methods suffer from computational inefficiency and objective mismatches between training and inference.<n>We introduce DiRL, an efficient post-training framework that tightly integrates FlexAttention-accelerated blockwise training with LMDeploy-optimized inference.
  arXiv  Detail & Related papers  (2025-12-23T08:33:19Z)
• Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models [49.911784762244814]
  TraceRL is a trajectory-aware reinforcement learning framework for diffusion language models (DLMs)<n>We derive a series of state-of-the-art diffusion language models, namely TraDo.<n>TraDo-8B-Instruct achieves relative accuracy improvements of 6.1% over Qwen2.5-7B-Instruct and 51.3% over Llama3.1-8B-Instruct on mathematical reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-09-08T17:58:06Z)
• InternBootcamp Technical Report: Boosting LLM Reasoning with Verifiable Task Scaling [71.37579508777843]
  Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities.<n>To address this gap, we present InternBootcamp, an open-source framework comprising 1000+ domain-diverse task environments.
  arXiv  Detail & Related papers  (2025-08-12T05:00:00Z)
• R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science [70.1638335489284]
  High-level machine learning engineering tasks remain labor-intensive and iterative.<n>We introduce R&D-Agent, a comprehensive, decoupled, and framework that formalizes the machine learning process.<n>R&D-Agent defines the MLE into two phases and six components, turning agent design for MLE into a principled, testable process.
  arXiv  Detail & Related papers  (2025-05-20T06:07:00Z)
• DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning [56.887047551101574]
  We present DS-Agent, a novel framework that harnesses large language models (LLMs) agent and case-based reasoning (CBR) In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle. In the deployment stage, DS-Agent implements a low-resource deployment stage with a simplified CBR paradigm, significantly reducing the demand on foundational capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-02-27T12:26:07Z)
• PerfRL: A Small Language Model Framework for Efficient Code Optimization [14.18092813639534]
  In this paper, we introduce PerfRL, an innovative framework designed to tackle the problem of code optimization.<n>Our framework leverages the capabilities of small language models (SLMs) and reinforcement learning (RL)<n>Our approach achieves similar or better results compared to state-of-the-art models using shorter training times and smaller pre-trained models.
  arXiv  Detail & Related papers  (2023-12-09T19:50:23Z)
• MegaBlocks: Efficient Sparse Training with Mixture-of-Experts [19.541303844245835]
  MegaBlocks is a system for efficient Mixture-of-Experts (MoE) training on GPUs. We reformulate MoE in terms of block-sparse operations and develop new block-sparse GPU kernels. Our approach never drops tokens and maps efficiently to modern hardware, enabling end-to-end training speedups of up to 40% over MoEs.
  arXiv  Detail & Related papers  (2022-11-29T00:27:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.