# π₀.₅ with language supervision (Pi052)

Pi052 extends [Pi05](./pi05) with a trainable PaliGemma language head and a
runtime that alternates language generation with action generation. A single
checkpoint can predict a low-level subtask, optionally update memory or answer
visual questions, and condition its flow-matching action expert on that text.

Use Pi05 when you only need task-conditioned actions. Use Pi052 when the policy
must generate or consume intermediate language during a rollout.

## How Pi052 differs from Pi05

| Capability          | Pi05                                                   | Pi052                                                                             |
| ------------------- | ------------------------------------------------------ | --------------------------------------------------------------------------------- |
| Action model        | PaliGemma vision-language prefix + Gemma action expert | Same base architecture                                                            |
| Language head       | Not trained for runtime generation                     | Re-enabled and trained with text cross-entropy                                    |
| Action conditioning | Episode task                                           | Active low-level subtask plus normalized robot state                              |
| Training targets    | Flow-matching actions                                  | Flow actions, recipe-selected text, and optional FAST action tokens               |
| Dataset requirement | Standard images, state, actions, and task              | The same fields plus language annotations for every language capability you train |
| Rollout             | Direct task-to-action policy                           | Hierarchical task → subtask → action loop, with optional memory and VQA           |

Pi052 can initialize from a Pi05 checkpoint. The policy architecture remains
compatible, while Pi052 builds its own processors so recipe labels and FAST
labels are not silently replaced by the Pi05 processor stack.

## Install

Install LeRobot with the PI dependencies:

```bash
git clone https://github.com/huggingface/lerobot.git
cd lerobot
python -m venv .venv
source .venv/bin/activate
pip install -e ".[pi]"
```

The `pi` extra includes the PaliGemma/FAST dependencies. Install
`liger-kernel` for the supported fused training kernels; optional FlashRT
backends also require the Hugging Face `kernels` package and a supported CUDA
GPU.

## Prepare language-annotated data

Pi052 does not infer supervised subtasks from a normal LeRobot dataset during
training. The dataset must contain the language targets used by the selected
recipe in the optional `language_persistent` and `language_events` columns.

At minimum, annotate a continuous `subtask` timeline so each training frame has
an active low-level instruction. Add `memory`, VQA, interjections, and speech
annotations only if the recipe trains those capabilities.

The provided recipes are:

| Recipe                                | Required annotations                                                    | Trains                                             |
| ------------------------------------- | ----------------------------------------------------------------------- | -------------------------------------------------- |
| `recipes/subtask.yaml`                | `subtask`                                                               | Subtask prediction and subtask-conditioned actions |
| `recipes/subtask_mem.yaml`            | `subtask`, `memory`                                                     | Subtasks, actions, and memory updates              |
| `recipes/subtask_mem_vqa_speech.yaml` | `subtask`, `memory`, `vqa`; interjection/speech rows for those branches | Subtasks, actions, memory, VQA, and spoken replies |

Use `lerobot-annotate` to generate these columns. The repository includes a
Hugging Face Jobs launcher that you can edit for your source and destination
datasets. For a local annotation run, first install
`pip install -e ".[annotations]"`:

```bash
HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
```

Before a long training run, inspect several episodes and verify that subtasks
are temporally correct and cover the full demonstration. See
[Annotation Pipeline](./annotation_pipeline) for generation and validation, and
[Language Columns and Recipes](./language_and_recipes) for the schema and
recipe resolver.

<Tip>
  If a dataset has no language columns, recipe rendering becomes a no-op and
  Pi052 falls back to the plain Pi05 prompt path. This is useful for
  compatibility but does not train the language planner.
</Tip>

## Train Pi052

This example initializes Pi052 from the public Pi05 base checkpoint and trains
the default subtask-and-memory recipe:

```bash
lerobot-train \
    --dataset.repo_id=${HF_USER}/my_language_annotated_dataset \
    --policy.type=pi052 \
    --policy.pretrained_path=lerobot/pi05_base \
    --policy.recipe_path=recipes/subtask_mem.yaml \
    --policy.dtype=bfloat16 \
    --policy.device=cuda \
    --policy.freeze_vision_encoder=false \
    --policy.gradient_checkpointing=true \
    --batch_size=8 \
    --steps=30000 \
    --output_dir=outputs/pi052 \
    --job_name=pi052 \
    --wandb.enable=true
```

For subtask-only data, change the recipe to `recipes/subtask.yaml` and disable
memory during rollout. Start with a small run and confirm that W&B examples show
the expected prompt, text target, and action endpoints before scaling up.

### Main training controls

| Option                           |                    Default | Purpose                                                        |
| -------------------------------- | -------------------------: | -------------------------------------------------------------- |
| `policy.recipe_path`             | `recipes/subtask_mem.yaml` | Selects the language/action objective mixture                  |
| `policy.text_loss_weight`        |                      `1.0` | Language-head cross-entropy weight; `0` disables text training |
| `policy.flow_loss_weight`        |                     `10.0` | Continuous action flow-loss weight                             |
| `policy.enable_fast_action_loss` |                     `true` | Adds discrete FAST action-token supervision                    |
| `policy.fast_action_loss_weight` |                      `1.0` | FAST cross-entropy weight                                      |
| `policy.knowledge_insulation`    |                     `true` | Blocks action-loss gradients through the VLM K/V path          |
| `policy.flow_num_repeats`        |                        `5` | Reuses one VLM prefix for independent denoising targets        |
| `policy.lm_head_lr_scale`        |                      `1.0` | Scales language-head learning rate; `1.0` uses the base rate   |

The loss weights are starting points, not dataset-independent constants. Track
flow loss and text/FAST losses separately, and inspect generated subtasks rather
than selecting a checkpoint from total loss alone.

### Dataset-specific FAST tokenizer

The universal FAST tokenizer works out of the box. For a large or
embodiment-specific dataset, Pi052 can fit and cache a tokenizer on normalized
actions before training:

```bash
lerobot-train \
    ... \
    --policy.auto_fit_fast_tokenizer=true \
    --policy.fast_tokenizer_fit_samples=4096
```

The fit runs once per dataset/tokenizer configuration. Keep
`auto_fit_fast_tokenizer=false` when you do not want the extra preprocessing
pass.

## Training performance

Pi052 uses optimized training paths by default:

- batches repeated flow targets and suffix projections instead of replaying
  small operations in Python;
- caches constant action masks and computes RoPE positions once per forward;
- selects the text/FAST cross-entropy implementation from target shape and
  sparsity;
- skips the mathematically dead VLM/vision backward on knowledge-insulated,
  flow-only batches;
- uses native non-reentrant SigLIP layer checkpointing when gradient
  checkpointing is enabled; and
- retains the Liger RoPE/GeGLU kernels while avoiding the slower LayerNorm
  patch at SigLIP shapes.

Optional training backends are disabled by default:

| Option                                 | When to try it                                                                                    |
| -------------------------------------- | ------------------------------------------------------------------------------------------------- |
| `policy.use_flashrt_adarms=true`       | Fused adaptive RMSNorm and gated residuals on supported CUDA GPUs                                 |
| `policy.use_compiled_text_ce=true`     | Compiled materialized-logit CE buckets                                                            |
| `policy.use_compiled_vision=true`      | Compiled vision only when the vision pass has no gradients                                        |
| `policy.use_flex_attention=true`       | Profiled CUDA setups with knowledge insulation and `flow_num_repeats > 1`; otherwise SDPA is used |
| `policy.use_manual_attention=true`     | Explicitly profiled shapes where materialized attention is faster                                 |
| `policy.manual_attention_scope=action` | Restricts manual attention to action queries                                                      |

Do not enable every backend blindly. Flex and manual attention are mutually
exclusive, and attention/AdaRMS alternatives require knowledge insulation.
The benchmark-best configuration used compiled text CE and FlashRT AdaRMS,
with Flex/manual attention and compiled vision disabled.

### Reported training benchmarks

These benchmarks measure complete optimizer steps with three real camera
inputs, BF16 transformer/action execution, FP32 vision, fused AdamW, and no
video decoding or network I/O. Results vary with GPU, batch shape, annotation
mixture, and checkpointing:

| Workload                   |     RTX PRO 6000 Blackwell |                 A100 80 GB |
| -------------------------- | -------------------------: | -------------------------: |
| Full flow + text, batch 1  | 4.75× vs checkpointing off | 3.33× vs checkpointing off |
| Full flow + text, batch 8  | 2.16× vs checkpointing off | 1.66× vs checkpointing off |
| Full flow + text, batch 64 |  1.24× vs checkpointing on |  1.15× vs checkpointing on |
| Flow-only, batch 1         | 3.70× vs checkpointing off | 3.58× vs checkpointing off |
| Flow-only, batch 64        |  3.76× vs checkpointing on |  3.61× vs checkpointing on |

On those 80 GB GPUs, full training was fastest without gradient checkpointing
through batch 8, then required checkpointing at batch 16 and above. Treat that
as a tuning rule to test on your hardware, not a universal threshold. Flow-only
means both text and FAST supervision are disabled; it is useful for action-only
ablation or post-training but does not learn the language runtime.

## Inference performance

Pi052 has two inference loops, and both avoid repeatedly encoding the expensive
multimodal prefix:

1. **Action denoising** encodes the image/language prefix once, reuses its KV
   cache across flow steps, precomputes the timestep schedule on-device, and
   crops temporary suffix K/V instead of cloning the prefix cache.
2. **Language decoding** uses autoregressive KV caching, so each new token only
   processes the sampled token against cached image/language keys instead of
   rerunning the full prefix.

The runtime also runs language and actions at different rates. Increase
`--subtask_chunks_per_gen` when a subtask remains valid across several action
chunks, lower `--high_level_hz`, or use `--direct_subtask` to bypass language
generation entirely. These settings reduce compute but also slow replanning.

`--fp8` enables the optional FlashRT inference MLP swap on supported CUDA GPUs.
It calibrates on the first observation and falls back to BF16 when unavailable;
because FP8 can change outputs slightly, validate task success before using it
for production rollouts.

## Run a checkpoint

RoboCasa:

```bash
MUJOCO_GL=egl lerobot-rollout \
    --policy.path=lerobot/pi052_robocasa \
    --sim --sim.task=CloseFridge --sim.split=pretrain \
    --task="close the fridge" \
    --disable_memory \
    --sim.render_size=384 \
    --sim.views=robot0_agentview_left,robot0_eye_in_hand,robot0_agentview_right \
    --mode=action --ctrl_hz=20
```

Open `http://localhost:8010` for the live view. Without
`--sim.direct_subtask`, Pi052 generates the low-level subtask; with it, each
prompt becomes the action policy's subtask directly.

The same runtime supports real robots. See [Interactive language
control](./inference#interactive-language-control) for the real-arm command,
safety behavior, and runtime controls.

## Troubleshooting

- **No text loss or generated subtasks:** confirm the selected recipe can bind
  the annotations on sampled frames and that `policy.text_loss_weight > 0`.
- **Subtasks look plausible but actions fail:** verify subtask boundaries,
  normalized state/action statistics, and that low-level recipe samples are
  present.
- **Text collapses to repeated or location tokens:** inspect text-target
  coverage, language-head learning rate, and the balance between flow, FAST,
  and text losses.
- **Out of memory:** reduce batch size first, then enable gradient
  checkpointing. Do not enable compiled or alternative attention backends
  without profiling their memory on your camera count.
- **Slow rollout:** separate action latency from language latency, then tune
  `--subtask_chunks_per_gen`, `--high_level_hz`, and the number of flow
  inference steps.
