0806 / 0807 speed-ablation checkpoints

Five checkpoints published to Alan0928/VLAwithVariousSpeed under checkpoints/<config>/<exp>/30000/, the same layout the repo already used for the 0510/0511/0513 generation.

All five share the training recipe, so any difference between them is the thing being ablated and nothing else:


Base weights	`pi05_base` (`pi0_fast_base` for the FAST variant)
Speeds	0.75, 1.0, 1.25, 1.5 (online sliding chunks)
Steps	30,000
Batch	512
LR	cosine, 1,000 warmup, peak 1e-4, decay to 1e-6
Optimizer	AdamW, grad-norm clip 1.0, EMA 0.999
Action horizon	30
Asset id	`online_sliding_speed_embed_0p75_1p0_1p25_1p5_pi05`

Norm stats are the source 1.0x statistics for every speed, so all five ship an identical norm_stats.json — see the norm-stats note in CLAUDE.md for why that is deliberate.

What each one isolates

Config / exp	Varies	Source branch
`pi05_libero_speed_embed_text` `0806_..._speed_embed_text_...`	reference condition — pi0.5 flow matching, speed as instruction text	`0809_ablation_eval`
`pi05_libero_speed_embed_none` `0806_..._nospeed_...`	no speed conditioning. Same multi-speed data, but no prompt rewrite and no model-side speed module, so the policy has to average over speeds. Adds no new parameters.	`0809_ablation_eval`
`pi05_libero_speed_embed_text_noseg` `0806_..._noseg_text_...`	no motion segmentation in the re-timer. Isolates what homogeneous-motion segmentation contributes; speed still arrives as text.	`0809_ablation_eval`
`pi05_libero_speed_embed_text_regression` `0806_..._regression_text_...`	L1 action regression instead of flow matching (learnable action queries, no timestep, no adaRMS).	`0809_arch_experiments_eval`
`pi0_fast_libero_speed_embed_text` `0807_pi0fast_...`	pi0-FAST — autoregressive over FAST action tokens. `action_dim=32` and `max_token_len=250` are load-bearing (see the config comment).	`0809_arch_experiments_eval`

LIBERO results

Overall success rate across all four suites, 500 episodes per suite (2,000 per speed), 50 trials per task. steps is mean_steps_all.

Checkpoint	0.75x	1.0x	1.25x	1.5x
`text_noseg`	95.9% / 212 steps	97.6% / 159	97.4% / 136	96.6% / 127
`none` (no conditioning)	—	96.8% / 152	—	—
`text_regression`	92.2% / 224	93.2% / 168	93.0% / 147	90.7% / 141
`pi0_fast`	91.0% / 225	93.5% / 167	—	—
`text` (reference)	—	—	—	—

The table is incomplete, and the gaps are missing evals rather than failures:

The reference text checkpoint has no eval results at all in either tree. Every comparison above is therefore against a missing baseline — the ablations cannot yet be read as deltas.
none was only run at 1.0x. That is the one speed where "no conditioning" is not obviously handicapped, so it is the least informative point; 0.75/1.25/1.5 are the ones that would show whether conditioning matters.
pi0_fast is missing 1.25x and 1.5x.

Per-suite numbers, per-episode JSON and run manifests are committed under results/ablation_0806/ and results/ablation_arch/ on the two branches. Rollout videos are gitignored (778M / 1.1G of mp4) and are not on HF either.

What is not published

optimizer.pt is not uploaded for these five, unlike the 0510/0511/0513 checkpoints which do carry theirs. It is ~13GB each and only matters if you resume training; skipping it took this upload from ~94GB to ~35GB. The files are still on the workstation if a resume is ever needed.

Using one

hf download Alan0928/VLAwithVariousSpeed \
  --include "checkpoints/pi05_libero_speed_embed_text_noseg/*" \
  --local-dir ./hf_ckpts

TORCH_COMPILE_DISABLE=1 CUDA_VISIBLE_DEVICES=0 uv run python scripts/serve_policy.py policy:checkpoint \
  --policy.config pi05_libero_speed_embed_text_noseg \
  --policy.dir ./hf_ckpts/checkpoints/pi05_libero_speed_embed_text_noseg/0806_pi05_online_sliding_noseg_text_0p75_1p0_1p25_1p5/30000 \
  --policy.asset-id online_sliding_speed_embed_0p75_1p0_1p25_1p5_pi05 \
  --port 8000

TORCH_COMPILE_DISABLE=1 is not optional — the training configs use pytorch_compile_mode='max-autotune' and leaving compile on stalls server startup for 5–15 minutes.

Serving the regression and pi0-FAST variants additionally needs the load_pytorch architecture dispatch from the 0809_arch_experiments_eval branch; without it the loader always builds PI0Pytorch and dies on a state_dict mismatch.

Reproducing the upload

HF_TOKEN=... vpn python3 scripts/upload_checkpoints_to_hf.py

Idempotent — size-matched files are skipped, so a run interrupted by a proxy drop can just be re-run. vpn is needed because huggingface.co is not reachable directly from the office network.