LoRA SFT (rank=32, alpha=64, LR=1e-4, seq=16384, 1 epoch) merged into google/gemma-4-31B-it. One point in a rank x LR sweep on the Rovo Chat Orchestrator tool-calling task.

Across the rank x LR grid, learning rate dominates both accuracy and stability: every LR=5e-5 config beats every LR=1e-4 config on overall AA and runaway rate. Instability is approximately (adapter capacity x LR) = how far the weights travel from the RLHF-aligned base. The sweet spot is r=16, LR=5e-5 (sibling repo tzchen07/gemma4-31b-rovochat-lora-r16-lr5e5).

No fine-tune beats the base -it model overall — narrow-domain tuning erodes the base's tool-vs-text calibration faster than it gains tool accuracy. Base wins on text AA, overall, and stability; the best tune improves only tool AA (+3 pts). Companion full-FT analysis (LR is the killing factor): tzchen07/gemma4-31b-rovochat-sft-v3.