---
license: apache-2.0
tags:
- robotics
- vla
- pi0.5
- libero
- openpi
- counterfactual
- flow-matching
---

# M2 — Train-Time Counterfactual Flow (RoboWarp)

Training-time internalization of RoboWarp's test-time CF-Steer: π₀.₅ finetuned so that the
flow's **source distribution** is tilted toward the policy's own counterfactual (attention-
erased) behavior, making the regressed velocity the prior→evidence correction. Trained with
[openpi](https://github.com/Physical-Intelligence/openpi) (JAX) on 2× NVIDIA GH200.

**Contents:** `params/` (Orbax weights) + `assets/` (the released pi05_libero quantile
normalization stats — required at inference). No `train_state/`, so this cannot be resumed.

## Method (M2)

1. **Evidence map** — RoboWarp CF-Steer masking on the frozen released `pi05_libero`:
   layer-12 suffix→image attention, register-mask + spike cleaning (ratio 2.0, 15 iters,
   σ 0.8), smoothed, thresholded at the 0.85 quantile (top-15% pixels; measured coverage 15.2%).
2. **Counterfactual prior** — `a_prior = pi05_libero(o₋)` with the attended pixels greyed
   (fill 128, camera still present), 10-step flow sample, normalized action space.
   `prior_norm_mean = 1.453`.
3. **Tilted source** — with probability p, `x₁ = α·a_prior + √(1−α²)·ε` (else `ε ~ N(0,I)`),
   so the CFM target on tilted samples is `α(a_prior − a) + √(1−α²)ε`: prior behavior earns
   zero loss reduction; only evidence-dependent corrections remain learnable. Inference is
   unchanged single-pass from N(0, I).

## Hyperparameters

| | |
| --- | --- |
| Init & drafter | `gs://openpi-assets/checkpoints/pi05_libero` (released; drafter = init) |
| Model | π₀.₅ (`pi05=True`, action_horizon 10, `discrete_state_input=False` — state-blind, matching the released checkpoint and the RoboWarp paper baseline) |
| Data | `physical-intelligence/libero` v2.0, **LIBERO-Spatial suite only**: 432/1693 episodes, 52,970 frames (task indices 30–39) |
| Normalization | released pi05_libero **quantile** stats (from the checkpoint's own assets) |
| Coupling | α = 0.6, p = 0.5 |
| Batch / steps | 160 (2× GH200, FSDP) / 2,300 (≈6.9 epochs) |
| LR | 3.95e-5 → 3.95e-6 cosine, warmup 200 (sqrt-rescaled from 256@5e-5) |
| Optimizer | AdamW β=(0.9, 0.95), eps 1e-8, wd 1e-10, grad-clip 1.0, EMA 0.999 |
| Masking params | layer 12, erase_pct 0.85, fill 128, ratio 2.0, iters 15, σ 0.8, attn from 2 denoise steps |
| Prior sampling | 10 Euler steps, seed 7000, batch 256 |
| Precision | bfloat16 |

## Caveats

- **Training loss is not comparable** to a standard-CFM baseline — tilted sources shrink
  targets on prior-correct states. Evaluate by simulator success rate (LIBERO-PRO
  spatial/swap) only.
- Not evaluated in sim at upload time. ~6.9 epochs (vs ~48 in the reference recipe).
- State-blind by design (matches the RoboWarp test-time baseline for comparability).
- Comparison runs: `spatial_run1` (plain finetune of pi05_base on the same data, HF:
  `adipotnis/pi05-libero-spatial-run1`); CF-Steer test-time numbers in the RoboWarp report.