training/RESULTS.md

CounterFeint - Training Results

Live tracking of every baseline + training run. Append rows as runs finish.

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Baseline (BEFORE training)

Hardware: T4 medium (HF Spaces), 4-bit quantisation, no fine-tuning.

Model	task_1	task_2	task_3	Mean	Fallback Rate	Run Date
Qwen/Qwen3-0.6B	0.543	0.576	0.180	0.433	83.51%	2026-04-26

Source: baseline_outputs/qwen3-0.6b/baseline_results.json on HF Space QuantumTransformer/CounterFeint-train (path /data/baseline_outputs/).

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Trained (AFTER training)

Model + Config	task_1	task_2	task_3	Mean	Delta vs base	Run Date
pending Qwen3.5-2B demo r1	-	-	-	-	-	-

Source: outputs/<TRAINED_TAG>/eval_summary.json on HF Space (path /data/outputs/).

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Notes

• Fallback rate = % of LLM calls that produced invalid JSON / wrong schema and fell back to ScriptedInvestigator. High fallback rate at baseline = strong learning signal for GRPO.
• task_3 is hardest (24 ads + cross-ad linking via link_accounts). 0.6B baseline of 0.18 is expected — small models can't handle the link-accounts logic without training.