# LoRA vs Full Fine-Tuning — Flappy (fixed_l2) Ablation

Evaluation results for an ablation comparing **LoRA** vs **full-parameter** fine-tuning of
`StarVLA/Qwen3-VL-4B-Instruct-Action` (openvla framework) on the Flappy environment at a
**fixed latency of 2 raw frames (66.67 ms)**.

## Result (latency 2, 50 episodes, seed 42)

| Method   | Mean reward | Std   | Mean survival steps | Final eval CE loss | Model |
|----------|-------------|-------|---------------------|--------------------|-------|
| **Full FT** | **18.52** | 18.93 | 167.6 | 0.060 | [openvla_flappy_l2_full](https://huggingface.co/latency-sensitive-bench/openvla_flappy_l2_full) |
| **LoRA** (r=32, α=64) | **3.86** | 0.27 | 49.6 | 0.224 | [openvla_flappy_l2_lora](https://huggingface.co/latency-sensitive-bench/openvla_flappy_l2_lora) |

**Full fine-tuning outperforms LoRA by ~4.8× in reward and survives ~3.4× longer.** On this
imitation task (CE on a Sample-Factory teacher), LoRA's low-rank update underfits relative to
full FT, consistent with the higher held-out CE loss.

## Controlled setup (everything identical except the variable under test)

- **Data**: `latency-sensitive-bench/flappy_fixed_l2_fs4` (990 episodes, 948,645 frames), frame stack 4.
- **Loss**: cross-entropy (categorical 2-action head). **Steps**: 2000. **Seed**: 42.
- **Effective (mini) batch = 64** on both sides, tuned per GPU to fill memory:
  - Full FT: per_device 32 × grad_accum 2 (single B200, ~151 GB peak).
  - LoRA: per_device 16 × grad_accum 4 (single A100-80GB, ~73 GB peak; 32 OOMs).
- **Learning rate** (the only deliberately method-specific knob): VLM backbone LR
  **2e-5 (full)** vs **2e-4 (LoRA, ~10×)**; the from-scratch action head is **1e-4 for both**.
  Rationale: full FT updates all pretrained weights (small LR avoids catastrophic forgetting);
  LoRA's effective update is scaled by α/r and needs a ~10× larger LR — a shared LR would
  under-tune LoRA and bias the comparison.
- **Eval**: `latency_bench.run`, `fixed` latency = 2, 50 episodes. Both models were evaluated on
  the same A100 hardware for an identical eval environment (reward is deterministic given seed).

## Files
- `full/queue_eval_latency_2.json`, `full/episode_metrics.jsonl`
- `lora/queue_eval_latency_2.json`, `lora/episode_metrics.jsonl`