ichenney/laya-browser-v32b

laya-browser-v32b (v32b-b15)

A 322M-parameter browser-agent decision model, fine-tuned from `cklxx/laya-browser` (which fine-tuned `convaiinnovations/laya` for browser decisions). Fully Apache-2.0 upstream; this checkpoint keeps the same license.

What it is: a System-1 decision head for browser agents. It never writes text — it answers typed questions about a page state (which operation? which element? does the goal-holding statement hold?) and returns calibrated probabilities. That makes it safe to put in front of a browser: it can only choose from what it was shown.

Headline numbers (all measured 2026-09-25, same fixtures for every model):

vs the official browser-tuned checkpoint: wins holdout (+28.8pp), MiniWoB (+25pp), suite v4 (+1.4pp), JevBench hard (+17.1pp), injection_safety (0.75 vs 0.50); loses suite v5 by 2 items. vs the hosted Jev API: lower absolute accuracy but $0 cost, fully private, offline, 31x faster — and it beats Jev on score questions (0.667 vs 0.333) and temporal_numeric (0.33 vs 0.20) on JevBench hard.

What was trained

Starting from the official v10s-lineage browser checkpoint, this model adds (v17→v32 recipe, frozen encoder + head-only training throughout):

1. 1.SCROLL_UP + recovery data — the official checkpoint had a 0/203 hit rate on scroll-up; this one is 193/203 with no loss of general accuracy.
2. 2.Counterfactual ranking pairs — Sharper target choice among similar candidates.
3. 3.Noul (statement-holds) training — the upstream training pipeline never produced noul items (yes/no judgment questions), leaving donejudgment at 0.615. A purpose-built 8k-item noul corpus (8 patterns × 320, balanced yes/no) raised probe accuracy from **37% → 100%** and donejudgment to 0.769 (probability-averaged with the choice specialist).
4. 4.Head blending — capability heads are blended into the champion at low weight (6–15%) so new skills arrive with zero regression elsewhere.

The encoder (134 tensors) is byte-identical to upstream; only the 36-tensor decision head was trained.

Usage

Requires `laya` (pip install laya) or `laya-mlx` on Apple Silicon.

import laya

agent = laya.load("ichenney/laya-browser-v32b", subfolder="v32b", device="cuda")
state = {...}  # page state: url, title, text, elements table
questions = {
    "operation": {"type": "choice",
                  "instructions": "Pick the operation that achieves the goal.",
                  "criteria": {"0": "CLICK", "1": "TYPE_TEXT", "2": "SCROLL_DOWN",
                               "3": "SCROLL_UP", "4": "SELECT_OPTION", "5": "WAIT",
                               "6": "DONE", "7": "BLOCKED"}},
}
answers = agent.predict(state, questions)["answers"]
print(answers["operation"]["choice"], answers["operation"]["probabilities"])

Or through the `localdecide` harness (recommended — it adds element-table building, coarse-to-fine chunking, and guards). v32b is the harness's default model — model="browser" loads this repo automatically:

from localdecide import BrowserDecider
from localdecide.backends import LayaTorchBackend

backend = LayaTorchBackend()          # model="browser" → this checkpoint
# to stay on the upstream cklxx v10s instead:
# backend = LayaTorchBackend(model="browser-legacy")
decider = BrowserDecider(backend=backend)

Checkpoint contents

v32b/
├── model.safetensors      # full model: frozen encoder (134 tensors) + trained head (36)
├── rl_agent_config.json   # laya agent config (max_len 1024, head_max_len 768, v1 format)
├── encoder/config.json    # ModernBERT-style encoder architecture config
└── tokenizer/             # tokenizer.json + config (same vocab as upstream)

Training details

• —Base: `cklxx/laya-browser` (frozen encoder — 134 tensors untouched, byte-identical), head-only fine-tune (36 tensors)
• —Data (each family generated synthetically from real page fixtures, no user data): | corpus | items | purpose | |---|---:|---| | general browser decisions (v10s-lineage mix) | ~11,800 | retain upstream competence | | targeted 6-family patch (op-choice / state-reason / multistep / surface-trap / injection / filter-first, 8 languages) | 9,600 | v20: fix known failure axes | | counterfactual ranking pairs | 11,200 | v21: sharper target choice (blend-only) | | SCROLLUP + recovery | 6,972 | v23: upstream had 0/203 scroll-up probe | | multistep replanning | 5,600 | v25: proved harmful, archived | | 22-failure-mode patch | 6,300 | v29: evidence for the noul investigation | | **noul statement-holds** | **8,000** (3,200 yes / 4,800 no) | **v31: the root-cause fix** — 8 patterns × 320, no-heavy to counter the model's yes-bias (probe NOULNO was 37%) |
• —Optimization: head LR 1e-4, bf16 autocast, batch 8, GRPO-style grouped advantages (group 4, wsph 0.75 / wrps 1.0), 1–3 epochs per stage
• —Cost: ~3 minutes per training run on one RTX 3080; the entire 15-version campaign totals under 2 GPU-hours
• —Method: single-variable iterations — change one thing, run the full eval battery (browser-suite v4/v5 + recovery2-holdout, ~90 min), keep or discard on data. New capabilities are trained as separate heads and blended into the champion at 6–15% weight, which transfers skills with zero measured regression.
• —Champion selection: highest held-out mean across the 3 suites; the final ensemble (v36) probability-averages v32b-b15 with the v31a noul specialist at w=0.4
• —Evaluation: 3 suites × 3 official baselines + hosted Jev API (fresh 231-item run), all JSONs published in the training log

Limitations

• —322M parameters: complex multi-hop reasoning (JevBench multi_hop 0.22 vs hosted Jev 0.89) is beyond current capacity — this is a capacity, not a recipe, gap
• —English-dominant training data (the synthetic fixtures cover 8 languages, but natural-page diversity is English-heavy)
• —Safety-adjacent judgments (phishing, abuse) should be guarded by harness rules, not trusted to the head alone — the v10s phishing analysis in the harness README applies to v32b too
• —FP32 weights: 1.3 GB on disk. An FP16/BF16 export would halve that; the MLX runtime loads it fine on 8 GB Apple Silicon but the torch path wants ~2 GB resident

License

Apache-2.0 (inherited from both upstream checkpoints). The companion harness is github.com/ChenneyZhuang/laya-browser-agent.

Citation

@misc{laya-browser-v32b,
  title  = {laya-browser-v32b: a noul-capable browser decision model},
  author = {Chenney Zhuang},
  year   = {2026},
  url    = {https://huggingface.co/ichenney/laya-browser-v32b},
  note   = {Fine-tuned from cklxx/laya-browser (Apache-2.0), frozen-encoder head training with noul corpus injection}
}