qwen3.5-4b-base-finance-head-50k

Paper: S²-MAS. The fixed-budget adaptation results and matched downstream methods define the comparison reported here.

Project background

S²-MAS is a society-scale multi-agent simulation framework for studying how household decisions affect consumption, cash reserves and payments. The paper connects a constructed resource of 500 million household-persona records, learned decision policies, reusable financial scenarios and a state-transition engine. Its purchase-funding application evaluates 12 million distinct synthetic household agents under paired conditions. Full-data Fin-Laya supplies the policy for that application; this repository supplies a fixed-budget adaptation checkpoint for the common decision-model comparison. Decision learning uses real-survey data. The GENESE/MAS engine implementation remains private.

The benchmark groups twelve systems into six decision-model routes: encoder option scoring; language models with learned decision heads; next-token decision SFT; dynamic-schema classification; multimodal encoder decisions; and provider decision interfaces. All receive text inputs in the common benchmark. The 17 model-repository entries comprise 10 fixed-budget trained variants—five native Laya and five Qwen variants—plus six checkpoint/runtime entries and one compatibility alias.

This model's role

This checkpoint follows the language-model plus learned decision-head route. The official Qwen3.5-4B-Base backbone stays frozen while a shared scalar head learns to score independently encoded candidates. It supplies the 50k frozen-backbone reference in the nested-budget comparison; development selects 100k cumulative presentations from the 250k terminal run.

Training design and checkpoint

Method: head_only.
Distinct downstream training rows: 50,000 from the fixed decision-QA training partition.
Initialization: Qwen/Qwen3.5-4B-Base.
This stage terminal row exposures: 250,000; selected checkpoint stage exposures: 100,000.
Selected checkpoint cumulative row exposures: 100,000; selected cumulative distinct rows: 50,000.
A fixed 20,000-example development set selects this checkpoint. The complete 412,873-example development partition compares the two 200k Qwen4B adaptation methods.

Performance and matched comparisons

Language: English. Inference precision: BF16 Qwen backbone with FP32 candidate scoring.

The common held-out test contains 240,458 QA records, 119 question versions and 8,275 respondent/household groups. The checkpoint is selected using development data before this test.

Checkpoint / reference	Accuracy (%)	Question-macro F1 (%)	Invalid decisions
This checkpoint	67.3369	31.0166	0
Qwen4B LoRA + head, same 50k unique QA	73.7289	36.6225	0

Accuracy weights every test record equally; invalid decisions count as incorrect. Question-macro F1 averages over every defined candidate class within each question version and then weights the 119 versions equally.

The matched LoRA checkpoint updates the backbone and shared scalar head using the same unique examples. This frozen-backbone checkpoint isolates learning in the decision head.

Package and inference

A shared scalar decision head on a frozen official backbone; obtain the official backbone separately.

The 4B initialization is the official pre-trained-only Base release.

Model files: head.pt.
Reported common-test compute: BF16 Qwen backbone with FP32 candidate scoring.
Obtain the official Qwen backbone and tokenizer named in the configuration separately.
Inference scores the full state, question and complete candidate menu.

The supplied qwen_decision.py exposes the candidate-scoring inference interface. Supply the local package and the official backbone and tokenizer named in decision_config.json.

License and access

The project model contribution is offered under the S²-MAS Controlled Model Research License v1.2: noncommercial scientific use, no redistribution, and no unauthorized reverse engineering, extraction or distillation. Training and evaluation records are not distributed. Source-data access requires the provider's authorization. Permission requests: yli9919@hku.hk.

Project repository: s2-mas.