The base-model criticism is fair. Laya is weak zero-shot and doesn't have the world knowledge of a much larger pretrained model. Their own site puts the base models at around 0.35 on the typed-decisions benchmark, which is close to random.
I don't think that makes it a dead end. Laya is designed to be fine-tuned for a specific task, and the site reports the fine-tuning gains. The 0.766 number comes from fine-tuning on the benchmark's train split, not from the base checkpoint. They also report that fitting a single temperature scalar per question type cuts expected calibration error from 0.466 to 0.081. That's a large gain, and it only shows up after you specialize the model.
"Peer" is doing a lot of work in that comment. Jev can take on a new task without retraining because it starts with far more knowledge; Laya trades that away to stay small and trainable for a fixed task. So I wouldn't compare base Laya to Jev and stop there. Compare Jev to fine-tuned Laya on the same task and test set, then look at accuracy, latency, cost, calibration, and robustness, depending on which of those matter for the deployment.
I don't think that makes it a dead end. Laya is designed to be fine-tuned for a specific task, and the site reports the fine-tuning gains. The 0.766 number comes from fine-tuning on the benchmark's train split, not from the base checkpoint. They also report that fitting a single temperature scalar per question type cuts expected calibration error from 0.466 to 0.081. That's a large gain, and it only shows up after you specialize the model.
"Peer" is doing a lot of work in that comment. Jev can take on a new task without retraining because it starts with far more knowledge; Laya trades that away to stay small and trainable for a fixed task. So I wouldn't compare base Laya to Jev and stop there. Compare Jev to fine-tuned Laya on the same task and test set, then look at accuracy, latency, cost, calibration, and robustness, depending on which of those matter for the deployment.