GoKawiilAWS engineer tests encoder vs decoder designs for Strands Decider model
Marc Brooker, an AWS engineer, responded to questions about why the strands-decider-2B model uses a modified decoder (LLM) architecture rather than a bidirectional encoder like BERT. He ran comparisons across five model designs, including the existing Qwen3.5-based decoder (v19), two encoder-decoder variants built on T5Gemma 2 at different parameter sizes, and encoder-only models based on T5Gemma. The post presents these as empirical tests of whether bidirectional encoders outperform causal decoders for this classification-style task.
GoKawiil's interpretation of the reporting above, not reported fact.
The comparison matters because it tests a common assumption in model design—that bidirectional context should beat causal, left-to-right processing for classification-like tasks such as pointer selection. Brooker's findings could inform whether AWS and other teams continue adapting decoder-only LLMs for tasks traditionally handled by encoder architectures, potentially simplifying model pipelines by reusing existing LLM infrastructure. Brooker frames his conclusions cautiously, noting his own limited expertise in model architecture research.
- AWS's strands-decider-2B uses a modified decoder architecture rather than a bidirectional encoder like BERT.
- Brooker compared five designs, including decoder-only, encoder-decoder (T5Gemma 2 based), and encoder-only variants at different parameter scales.
- The analysis directly addresses whether bidirectional token representations outperform causal ones for this specific pointer/classification task.
Source: brooker.co.za:443 — Marc Brooker, 2026-10-14
Published there as: “Strands Decider: Why Not an Encoder?”
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