Multi-Stage Progressive Training (MSPT)

Updated 8 July 2026

Multi-Stage Progressive Training (MSPT) is a framework that divides model training into sequential stages to gradually enhance performance.
The method employs progressive refinement of parameters, resulting in improved convergence rates and optimized evaluation metrics.
MSPT has practical applications in computer vision, natural language processing, and reinforcement learning, offering robust improvements over traditional techniques.

Searching arXiv for the cited MSPT-related papers to ground the article in current records. arXiv search query: (Li et al., 2020) OR (Zheng et al., 2022) OR (Chen et al., 2023) OR (Tan et al., 2021) OR (Rathod et al., 2022) OR (Ahmad et al., 2024) OR (Panigrahi et al., 2024) OR (Lu et al., 25 Jul 2025) OR (Xiao et al., 11 Aug 2025) OR (Lu et al., 2024) OR (Yang et al., 2020) OR (Mainuddin et al., 12 Jun 2026) OR (Bu, 7 Nov 2025) OR (Kumar et al., 15 Nov 2025) OR (Li et al., 9 Oct 2025) OR (Tu et al., 27 Oct 2025)

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References (16)

1.

Progressive Multi-Stage Learning for Discriminative Tracking (2020)

2.

Progressive Training of A Two-Stage Framework for Video Restoration (2022)

3.

Continual Multimodal Knowledge Graph Construction (2023)

4.

MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators (2021)

5.

Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition (2022)

6.

Progressive unsupervised domain adaptation for ASR using ensemble models and multi-stage training (2024)

7.

Efficient Stagewise Pretraining via Progressive Subnetworks (2024)

8.

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs (2025)

9.

MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training (2025)

10.

A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer (2024)

11.

Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup (2020)

12.

HumP-KD: A Hybrid Uncertainty-Aware Multi-Stage Progressive Knowledge Distillation Framework for Efficient Fire Classification (2026)

13.

Deep Progressive Training: scaling up depth capacity of zero/one-layer models (2025)

14.

DeiTFake: Deepfake Detection Model using DeiT Multi-Stage Training (2025)

15.

SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models (2025)

16.

A Survey on LLM Mid-training (2025)

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