3 SFT recipes finetuned on Alpamayo R1 base, each going through Stage 1
(VLM finetune, token CE) → Stage 2 (diffusion expert SFT, flow matching).
All 3 epochs, 8× H200, DeepSpeed ZeRO-2.
All recipes use the same camera scheme aligned to NVIDIA's 4-cam slots
[0, 1, 2, 6] = (cross_left, front_wide, cross_right, front_tele).
Waymo data is re-mapped to these slots; front_tele is emulated by
center-cropping Waymo FRONT (~30°/50.4°FOV ≈ 0.595 crop ratio).
Recipes
Recipe
NV component
LT component
Waymo component
R-A
10k random
1450 GT (real)
Waymo GT (real, 798 clips)
R-B
10k random
1450 Ours-synth
Waymo Ours-synth (Ray-WAN, 800 clips)
R-C
10k random
1450 GT (real)
Waymo Ours-synth (Ray-WAN, 800 clips)
Final losses (3 epochs each)
Recipe
Stage 1 (token CE)
Stage 2 (flow matching)
R-A
1.277
0.385
R-B
1.281
0.403
R-C
1.279
0.400
Files
Per recipe (e.g. R-A_s2/):
model-*-of-*.safetensors: model weights (~21 GB total)
model.safetensors.index.json: shard index
config.json: Alpamayo R1 model config
trainer_state.json: full training history (loss curve, lr schedule)
training_args.bin, scheduler.pt: trainer state
Optimizer states are NOT included (only useful for training resume).
Re-ran the Waymo synth pipeline with two improvements (waymo_p65v2_va_190308):
Variant A: source-view K = original Waymo intrinsics (target views still PAI K),
fixes the camera-pitch artifact that made the v1 synth look like it was tilted down.
Qwen3-VL-8B-Instruct per-clip caption: each clip gets its own caption from
Qwen3-VL rather than borrowing a PAI prompt that mentions unrelated objects.
Then re-trained R-B and R-C on this new synth (3 epochs each, identical schedule).
Recipe
Stage 1 (token CE)
Stage 2 (flow matching)
Subfolder
R-B v2
1.282
0.402
R-B_v2_s2/
R-C v2
1.280
0.398
R-C_v2_s2/
Loading v2:
model = AlpamayoR1.from_pretrained("luuuulinnnn/alpamyo_Waymo", subfolder="R-B_v2_s2")
