mlsanityData to 14 Oct 2026

ModTrans: Translating Real-world Models for Distributed Training Simulator

arXiv 2604.01607 ↗First posted 2 Apr 2026cs.DC cs.AIcited by 0

On the map: World models → World models for language agents

Abstract (arXiv)

Large-scale distributed training has been a research hot spot in machine learning systems for industry and academia in recent years. However, conducting experiments without physical machines and corresponding resources is difficult. One solution is to leverage distributed training simulators, but current ones like ASTRA-sim do not support importing real-world developed models, which poses challenges for ML researchers seeking to use them. Based on this challenge, we developed ModTrans, a translator supporting format translation from any real-world model to the ASTRA-sim simulator's input, removing the barrier between machine learning experts and machine learning system researchers. The experiment results show that ModTrans's cost is negligible.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 8 of its 14 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

8 of 14 references.

References · 8

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  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  4. 2024Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learningcited by 5
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  6. 2024CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inferencecited by 3
  7. 2025MonoM: Enhancing Monotonicity in Learned Cardinality Estimatorscited by 1
  8. 2026Safeguard: Security Controls at the Software Defined Network Layercited by 1

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  1. 2024MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation
  2. 2025DeepTrans: Deep Reasoning Translation via Reinforcement Learning
  3. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
  4. 2025Model-to-Model Knowledge Transmission (M2KT): A Data-Free Framework for Cross-Model Understanding Transfer
  5. 2023FlexModel: A Framework for Interpretability of Distributed Large Language Models
  6. 2025Knowledge Fusion of Large Language Models Via Modular SkillPacks
  7. 2026Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning
  8. 2026CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora