Laya Browser (Mind2Web fine-tune)

A Laya (421M, ModernBERT-large encoder + typed decision heads) checkpoint fine-tuned to pick the next browser action — an operation (CLICK / TYPE_TEXT / SELECT) and a target element — from a page's DOM and a natural-language goal. Trained on Mind2Web (Deng et al., NeurIPS 2023 Datasets and Benchmarks Track, CC-BY-4.0). Built as the policy for laya-system-use, a fork of browser-use/jev-ultrafast that replaces the hosted TypeSafe Jev API with this open-weight model.

This is a research/hobby-project checkpoint, not a production model. Read the Limitations section before using it.

What it does

Given a page's interactive elements (label, role, current value) and a goal, it answers two typed questions in one forward pass: which operation to perform, and which element to act on. It does not generate text — a separate small local LLM fills in typed values (TYPE_TEXT), and the element ranking is done by an untrained lexical shortlister before this model ever sees the candidates (see Methodology).

Methodology

Data pipeline

1. Parsing. Mind2Web's raw HTML/candidate format is converted into a neutral Candidate(id, label, role, value, ops) record. Labels come from the DOM (aria-label, text content, alt, title, placeholder, value, name, in that order), falling back to the element's role when none exist — matching what the live browser snapshot exports, so training and serving never see different inputs.
2. Shortlisting. Mind2Web pages average ~140-580 candidate elements; the fine-tuned model's context budget only fits ~20. An untrained lexical ranker (rare-word-weighted overlap between the goal/history and each candidate's label, plus a small role prior and page-position prior) keeps the top 20 per operation. This ranker was tuned on Mind2Web's training data only: it raised recall@20 from 0.686 to 0.755 (train) / 0.662 to 0.790 (dev) before any model training began.
3. Reclaiming non-interactive gold elements. ~15% of steps have a labeled target that isn't itself clickable/typeable (an icon inside a button, a <label> around an input). These are mapped to the nearest interactive descendant or ancestor in the DOM tree, recovering steps that would otherwise be unusable.
4. Case building. Each usable step becomes 1-2 typed questions (operation, and <op>_target when more than one candidate survives the shortlist). A step with exactly one candidate needs no question at all — the live agent skips the model entirely in that case, and training treats it as free.

Training

• Base: convaiinnovations/laya (ModernBERT-large, 421M params), unmodified.
• Data: Mind2Web train split, 8,613 usable typed-decision items (from 949 tasks, 69 websites), tokenized at max_len=768 / head_max_len=448 (zero items overflowed this budget; longest was 503 tokens).
• Method: RLCD — proper-scoring-rule policy gradient (GRPO-style noised-logit sampling) plus soft cross-entropy guidance, following Laya's own fine-tuning recipe.
• Hardware: Kaggle, 2× NVIDIA T4, DDP (torch.distributed, NCCL).
• Schedule: 4 epochs, micro-batch 8, grad-accum 4 (effective batch 64), encoder LR 2.5e-5, head LR 1e-4, cosine schedule, exploration noise σ 0.4→0.1 across epochs. Wall-clock: ~33 minutes.
• Calibration: post-training temperature fitting, per Laya's option-count bucket (choice:2, choice:3-5, choice:6-10, choice:11+), on held-out dev items — fitted values: [3.86, 2.96, 2.36, 2.16] respectively. (An earlier bug where the base checkpoint's own bucket temperatures silently overrode the fitted ones — making the dominant 11+-option bucket run at T=0.10, i.e. near one-hot overconfidence — was found and fixed before this checkpoint was trained; every bucket here is freshly fit.)
• Checkpoint safety: weights and a placeholder-calibration config are saved before calibration runs, so a calibration failure can never discard a finished training run.

What was tried and rejected

Two follow-up experiments were run and did not improve on this checkpoint — included here for honesty, not as a footnote:

• More epochs (8 instead of 4). Looked like a clear win on the held-out dev split (element accuracy 0.487 → 0.524 macro), but did not generalize: on the actual test splits it tied or lost to the 4-epoch checkpoint on 2 of 3 splits. Classic dev-set overfitting; not adopted.
• LoRA (rank 8, frozen base encoder, same schedule). Training loss stayed far higher throughout (~1.4-1.5 vs ~0.3-1.0 for full fine-tuning) and element accuracy on dev dropped to roughly zero-shot level (0.29, vs 0.67 for full fine-tuning). 0.9% trainable parameters was not enough capacity for this loss within 4 epochs at this rank/LR. Not adopted.

Evaluation

Evaluated on Mind2Web's three official, held-out test splits — cross-task, cross-website, and cross-domain generalization — with task counts matching the paper exactly (252 / 177 / 912 tasks). No sampling: every step of every task in each split was scored. Metrics follow the Mind2Web paper's protocol exactly: macro-averaged per task (a task's score is the mean correctness across all of its steps, then averaged across tasks), with success_rate requiring every step in a task to succeed. A step this model was never asked about (its gold element didn't survive the shortlist, or Mind2Web itself gave no usable candidate for it) counts as a hard failure for these macro metrics — the same standard MindAct implicitly holds itself to.

Element accuracy roughly triples over both baselines on every split, confirming the gain is from fine-tuning, not from the base checkpoint alone (zero-shot stays near chance throughout).

Against the published MindAct benchmark

MindAct (Deng et al., NeurIPS 2023, Table 2), best backbone (Flan-T5-XL), same macro protocol, pulled directly from the paper:

Competitive on element accuracy, behind on step/task success — for two identifiable, honest reasons (not spin):

1. Candidate recall is the ceiling. MindAct's trained DeBERTa-base ranker gets 85-89% Recall@50. This checkpoint's untrained lexical shortlister gets 73-79% Recall@20 — half the candidate budget, no training. Element accuracy cannot exceed what survives the shortlist.
2. op_acc isn't Operation F1. MindAct's Op F1 checks the value typed/selected, not just the action type. This model's text-filling component (a separate local LLM) isn't wired into this evaluation, so op_acc here only checks CLICK/TYPE_TEXT/SELECT correctness, not typed-value correctness. This makes the comparison to MindAct's Op F1 column directionally favorable to this checkpoint, not apples-to-apples.

Limitations

• Shortlist recall bounds everything downstream. ~21-27% of steps lose their correct element before this model ever sees it. Improving the shortlister (a trained ranker, a larger K) would likely help more than further fine-tuning.
• No typed-value evaluation. See above — op_acc is action-type accuracy only.
• Small, single-domain data. 8,613 items from one dataset, one training run, no cross-validation.
• Live-page gap untested. Evaluated entirely on Mind2Web's dataset snapshots, not on live, rendered pages through the actual browser agent. Distribution shift (viewport-only visibility vs. Mind2Web's full-page candidate lists, real-world DOM noise) is expected but not yet measured.
• Calibrated confidences, not calibrated correctness. Temperature fitting makes the confidence scores meaningful; it does not change which answer is chosen.

Usage

import laya

agent = laya.load("Quantum08/laya-browser-mind2web")
result = agent.system_one(state, questions)  # same typed-question API as upstream Laya / TypeSafe Jev

state and questions follow the format built by jev_ultrafast/formatter.py and jev_ultrafast/shortlister.py in the source repo — using this checkpoint directly with a different candidate format will not reproduce these numbers, since the shortlister and label-rendering are load-bearing parts of the pipeline, not just preprocessing.

License

Apache-2.0, inherited from the base model. Mind2Web is CC-BY-4.0 (Deng, Xin, Su, et al. "Mind2Web: Towards a Generalist Agent for the Web." NeurIPS 2023 Datasets and Benchmarks Track).