FinL · three-round comparator (native PyTorch)

Model naming and experiment labels

This is the FinL three-round comparator, in native Laya/PyTorch format. The main FinL name refers to the full six-round checkpoint. Its paired earlier MLX format remains fin-laya-mlx-3-rounds.

Project background

S²-MAS is a society-scale multi-agent simulation framework for studying how heterogeneous household decisions become population outcomes under changing conditions. It connects a constructed resource of 500 million household personas, reusable scenarios, trainable decision models and stateful world updates. FinL supplies a reusable decision policy: it scores the complete alternatives for a household state and question, allowing the same policy interface to serve different tasks.

The manuscript's paired-condition application evaluates 12 million distinct synthetic household agents and 24 million choices. A 24-month extension follows 10,000 households through changing income, expenses and bank offers. The applications show how household resources shape consumption responses: persistent income loss draws down reserves and delays purchases, while sustained income gains support both cash reserves and purchases. Repayment feedback affects later offers. These findings connect individual choices to consumption, cash and payment paths over time.

Repository role and training

This is the native FinL 3-round financial checkpoint, with approximately 421M parameters, based on Laya's ModernBERT-large encoder and option-scoring head. Two SFT stages followed by native RLCD adapt the model to real-survey data, using 1,721,623 training observations per language. The MLX runtime packages this trained checkpoint for FP16 deployment. The 6-round continuation extends its supervised task coverage.

Paper results for this checkpoint

Test	Input language	Backend / precision	QA denominator	Question versions	Accuracy	Question-macro F1
Shared financial questions	English	PyTorch BF16	240,458	119	74.5336%	38.0164%
Expanded test	English	PyTorch BF16	418,597	862	56.3657%	19.2395%

Accuracy weights QA records equally. Question-macro F1 averages F1 over the complete candidate classes within each question version, then weights question versions equally.

On the same shared test, original Laya in native BF16 scores 60.2080% accuracy and 29.5574% F1; financial training raises these to the 3-round results above. See Figure 5 and Methods M4/M6.

Deployment timing

The manuscript's single-request timing uses the FinL-bilingual checkpoint on one NVIDIA A100 PCIe 40GB in FP16, with batch size and concurrency one and three fixed Chinese cases. Its card and Methods M9 report latency, throughput and the Jev comparison.

Model files and inference

This repository contains model weights, the tokenizer and the inference configuration. Native Laya / PyTorch inference uses BF16. predict_example.py uses an independently generated synthetic example.

License and manuscript

The project model contribution is offered under the Controlled Model Research License v1.2. The full license and retained upstream Apache-2.0 rights are in LICENSE. Training and evaluation records are not distributed with the model.

S²-MAS manuscript · Project

The public project provides scientific plotting, decision-model training and evaluation, and independent synthetic companion-data generation scripts. The GENESE/MAS engine implementation remains private.