SamatNext v0.2-B: An Exploratory Study of RMS-Normalized Hybrid Decoders for Curriculum Retention in Small Code Models
arXiv:2606.22248v2 Announce Type: replace Abstract: Standard autoregressive Transformer decoders can often exhibit substantial forgetting under sequential fine-tuning on shifting curriculum distributions. This technical report evaluates SamatNext v0.2-B, an experimental 356M-parameter hybrid sequence decoder that alternates Differential-Attention-style layers with DeltaNet-inspired simplified linear-state mixer layers using RMS normalization and output scale calibration. We study the model under a controlled staged Python code curriculum and compare it with a parameter-matched Transformer base
The continuous push for more efficient and robust AI models, especially in specialized domains like code generation, necessitates ongoing research into decoder architectures to overcome limitations like catastrophic forgetting.
Improving the 'curriculum retention' of small code models could significantly lower the barrier to entry for developing and maintaining specialized AI agents and tools, affecting productivity and innovation in software development.
This research suggests a potential pathway to more stable and adaptable small language models, capable of learning sequentially without losing prior knowledge, which could lead to more practical applications in constrained environments.
- · AI model developers
- · Software engineers using AI assistants
- · Edge AI computing
- · Specialized AI startups
- · General-purpose, resource-intensive large language models (to a degree)
- · Companies reliant on frequent, full retraining of models
- · Legacy AI model architectures
More stable and adaptable small code models become available for various development tasks.
Increased adoption of customized AI assistants and copilots within development workflows, potentially accelerating software delivery.
The proliferation of highly specialized, continuously learning AI agents impacting a wider array of white-collar work beyond just coding.
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Read at arXiv cs.LG