Laya: a 421M non-autoregressive System 1 decision model at 33 ms per forward pass
conval-laya
Convai Innovations' open-source non-autoregressive System 1 decision model: 421M parameters, ModernBERT backbone with [MASK] option extraction, one 33 ms forward pass per calibrated decision, Apache-2.0; trained with RLCD strictly proper scoring rules for honest probabilities; the model card ships a real benchmark against Jev plus an honest limitations list. Positioned for edge and high-concurrency small decisions (game NPCs, dialogue policy, request routing), not long reasoning or open generation.
- CONFIDENCE
- Vendor Claim
- Official model card or keynote only, no independent re-test
- KEY METRIC
- 单次前向延迟(421M,官方)
- Vendor Claim · 2026-09
- MATURITY
- Research
- research → demo → product → production
Our takeOur read: 421M parameters at 33 ms puts System 1 decisions inside real-time loops, Apache-2.0 weights make reproduction nearly free, and the card's head-to-head against Jev plus its limitations list are high-water marks for open releases. But non-autoregressive option extraction is capped by option-set quality, and RLCD calibration out of distribution still lacks third-party reproduction; using it as a decision co-processor rather than the main model is the correct posture. Confidence C (vendor claim).
Laya is Convai Innovations' open-source non-autoregressive System 1 decision model: 421M parameters, a ModernBERT backbone with [MASK] option extraction, one 33 ms forward pass per calibrated decision, weights and code released under Apache-2.0. It does not generate text; it extracts and scores over a given option set, compressing small decisions into a size that fits inside real-time loops.
Training uses RLCD with strictly proper scoring rules: the objective is not human preference ranking but statistically honest output probabilities, so confidences can be consumed as decision risk. That is the deepest split from RLHF-style decision heads and the clearest section of the model card.
The card ships a real benchmark comparison against Jev plus an honest limitations list (option-set coverage, out-of-distribution calibration gaps, multilingual tier differences) - one of the few open releases we have read that names a competitor directly; siblings laya-multilingual and laya-typed-decisions cover variant tiers.
Boundary: edge and high-concurrency small decisions (game NPCs, dialogue policy, request routing), not long reasoning or open generation; option-set quality caps the ceiling, and open-domain questions need a sequential model to write the candidate set first.