100-task meta -> 4-task finetune (54/56/77/98), terminal checkpoint 24999

.gitattributes

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pi_behavior_100t_meta_ft_4task/README.md

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+# PiBehavior — 100-task meta → 4-task finetune (BEHAVIOR-1K 2026)
+
+A flow-matching vision-language-action (VLA) policy for the **BEHAVIOR-1K 2026 challenge**, finetuned from the **100-task meta checkpoint** on **4 tasks**. RGB-only (no DA3 depth branch).
+
+## Model
+- **Architecture:** PiBehavior (the 2025 #1 IliaLarchenko `behavior_224_rgb` architecture), pi0-style flow-matching VLA.
+  - VLM backbone: **PaliGemma (gemma_2b)** · action expert: **gemma_300m** · compute dtype **bf16**.
+  - 100-task conditioning tables (task + stage embeddings); vision backbone frozen.
+- **Init:** the **100-task meta checkpoint** (`meta100-1epoch`, step **69999**). Task/stage tables are already 100-wide, so they load directly with **no expansion** (unlike the 50→100 sibling release).
+- **Finetuned on 4 tasks** (BEHAVIOR-1K activity indices):
+  - `54` putting_away_toys · `56` make_rose_centerpieces · `77` installing_a_modem · `98` laying_tile_floors
+- **Recipe:** 8×H200, batch **256**, FSDP=1 (data-parallel), **2 epochs** (25,000 steps; final checkpoint = step **24999**). LR cosine ramp `1e-8 → 5e-5` (by 1k) `→ 1e-6` (by 25k). **qvel-fixed** norm stats (bundled), fast tokenizer from the meta checkpoint.
+- **Data:** `b1k-224x224-gop8-fixed` (224×224 RGB, GOP8), 800 episodes (200 per task).
+
+## Relationship to the 50-task sibling
+This is the **100-task-meta** counterpart of `pi_behavior_50t_meta_ft_4task`: identical 4 tasks, data, and finetune recipe — the only difference is the initialization (100-task meta step 69999 here vs. the 50-task meta with 50→100 table expansion there). Use this pair to isolate the effect of the meta-pretraining breadth.
+
+## Contents
+- `params/` — Orbax model parameters (bf16). Load with the openpi `PiBehaviorWeightLoader`.
+- `assets/IliaLarchenko/behavior_224_rgb/` — **`norm_stats.json`** (qvel-fixed) + **`fast_tokenizer/`**. Required for eval.
+- (Optimizer `train_state` is NOT included — this is an inference/eval release. Available on request for resuming.)
+
+## Inputs / Outputs
+**Observation (input):**
+| field | shape | notes |
+|-------|-------|-------|
+| `images["base_0_rgb"]` | 224×224×3 uint8 | head / countertop camera |
+| `images["left_wrist_0_rgb"]` | 224×224×3 uint8 | left-wrist camera |
+| `images["right_wrist_0_rgb"]` | 224×224×3 uint8 | right-wrist camera |
+| `state` | 32 float | proprioception; **normalize with `norm_stats["state"]`** |
+| `prompt` | text | task instruction; tokenized by the bundled fast tokenizer |
+| `task_index` | int (0–99) | which of the 100 tasks (use the trained index) |
+
+**Action (output):**
+- `action`: **32-dim × 30-step** chunk (action horizon 30), produced by the flow-matching sampler, then **de-normalized with `norm_stats["actions"]`**.
+- Meaningful dims (23 of 32; rest are zero-padding): base velocity (3), trunk (4), left arm (7), right arm (7), left gripper (1), right gripper (1).
+
+## How to evaluate (properly)
+1. **Load** `params/` with the `pi_behavior` config (`num_tasks=100`, PaliGemma gemma_2b + gemma_300m, `da3=None`).
+2. **Assets:** point `assets_base_dir` at the bundled `assets/` so the model uses **these qvel-fixed** `norm_stats.json` + `fast_tokenizer` — do **not** substitute upstream/2025 norm stats (the action scale differs and will silently degrade rollouts).
+3. **Per step:** feed the 3 RGB views + `state` (normalized) + `prompt` + the task's `task_index`. Sample the 30-step action chunk via the flow ODE (10–15 denoise steps), de-normalize, execute (action-chunking / receding-horizon).
+4. **Benchmark:** run in the BEHAVIOR-1K simulator on the 4 tasks above; report task **success rate**. For a like-for-like comparison, evaluate only the 4 trained tasks (the model was not tuned on the other 96).
+
+## Notes
+- RGB-only baseline (no GT-depth / DA3). Its DA3 counterparts (camera-frame ray + cross-view) are separate checkpoints.
+- `fast_auxiliary` (FAST token head) was used during training; not required at inference.

pi_behavior_100t_meta_ft_4task/_CHECKPOINT_METADATA

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+{"item_handlers": {"assets": "b1k.training.checkpoints.CallbackHandler", "params": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler", "train_state": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler"}, "metrics": {}, "performance_metrics": {}, "init_timestamp_nsecs": 1787765460635282979, "commit_timestamp_nsecs": 1787765504562414673, "custom_metadata": {}}

pi_behavior_100t_meta_ft_4task/assets/IliaLarchenko/behavior_224_rgb/fast_tokenizer/metadata.json

@@ -0,0 +1,25 @@
+{
+  "vocab_size": 1024,
+  "scale": 10.0,
+  "encoded_dims": "0:6,7:23",
+  "encoded_dim_ranges": [
+    [
+      0,
+      6
+    ],
+    [
+      7,
+      23
+    ]
+  ],
+  "total_encoded_dims": 22,
+  "action_horizon": 30,
+  "num_training_chunks": 5935465,
+  "compression_stats": {
+    "compression_ratio": 3.644254501482549,
+    "mean_token_length": 181.107,
+    "p99_token_length": 658.0,
+    "min_token_length": 35.0,
+    "max_token_length": 660.0
+  }
+}

pi_behavior_100t_meta_ft_4task/assets/IliaLarchenko/behavior_224_rgb/fast_tokenizer/processing_action_tokenizer.py

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+import logging
+from typing import ClassVar
+
+import numpy as np
+from scipy.fft import dct
+from scipy.fft import idct
+from tokenizers import ByteLevelBPETokenizer
+from tokenizers.trainers import BpeTrainer
+from transformers import PreTrainedTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+
+class UniversalActionProcessor(ProcessorMixin):
+    attributes: ClassVar[list[str]] = ["bpe_tokenizer"]
+    bpe_tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        bpe_tokenizer: PreTrainedTokenizerFast,
+        scale: float = 10,
+        vocab_size: int = 1024,
+        min_token: int = 0,
+        *,
+        action_dim: int | None = None,
+        time_horizon: int | None = None,
+    ):
+        self.scale = scale
+        self.vocab_size = vocab_size
+        self.min_token = min_token
+
+        # Action horizon and dimension needed during decoding. These can be specified
+        # in three ways (in order of priority):
+        # 1. passed in as kwargs to decode()
+        # 2. in the constructor
+        # 3. cached from the last time decode() was called
+        self.time_horizon = time_horizon
+        self.action_dim = action_dim
+        self.called_time_horizon = time_horizon
+        self.called_action_dim = action_dim
+
+        super().__init__(bpe_tokenizer)
+
+    def __call__(self, action_chunk: np.array) -> np.array:
+        assert action_chunk.ndim <= 3, "Only 3 dimensions supported: [batch, timesteps, action_dim]"
+        if action_chunk.ndim == 2:
+            action_chunk = action_chunk[None, ...]
+
+        # Cache the time horizon and action dimension for decoding
+        self.called_time_horizon = action_chunk.shape[-2]
+        self.called_action_dim = action_chunk.shape[-1]
+
+        dct_coeff = dct(action_chunk, axis=1, norm="ortho")
+        dct_coeff = np.around(dct_coeff * self.scale)
+        tokens = []
+        for elem in dct_coeff:
+            token_str = "".join(map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int)))
+            tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
+        return tokens
+
+    def decode(
+        self,
+        tokens: list[list[int]],
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> np.array:
+        self.time_horizon = time_horizon or self.time_horizon or self.called_time_horizon
+        self.action_dim = action_dim or self.action_dim or self.called_action_dim
+
+        # Cache the time horizon and action dimension for the next call
+        self.called_time_horizon = self.time_horizon
+        self.called_action_dim = self.action_dim
+
+        assert (
+            self.time_horizon is not None and self.action_dim is not None
+        ), "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
+
+        decoded_actions = []
+        for token in tokens:
+            try:
+                decoded_tokens = self.bpe_tokenizer.decode(token)
+                decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.min_token
+                decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
+                assert (
+                    decoded_dct_coeff.shape
+                    == (
+                        self.time_horizon,
+                        self.action_dim,
+                    )
+                ), f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
+            except Exception as e:
+                print(f"Error decoding tokens: {e}")
+                print(f"Tokens: {token}")
+                decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
+            decoded_actions.append(idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho"))
+        return np.stack(decoded_actions)
+
+    @classmethod
+    def fit(
+        cls,
+        action_data: list[np.array],
+        scale: float = 10,
+        vocab_size: int = 1024,
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> "UniversalActionProcessor":
+        # Run DCT over all inputs
+        dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
+
+        # Quantize and find min token
+        max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
+        min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
+        min_vocab_size = max_token - min_token
+
+        assert (
+            min_vocab_size <= vocab_size
+        ), f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
+        if min_vocab_size + 100 > vocab_size:
+            logging.warning(
+                f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
+                f"size {vocab_size}, consider increasing vocab size"
+            )
+
+        # Make token iterator for BPE training
+        def _token_iter():
+            for tokens in dct_tokens:
+                rounded_tokens = np.around(tokens * scale) - min_token
+                rounded_tokens = rounded_tokens.astype(int)
+                string = "".join(map(chr, rounded_tokens))
+                yield string
+
+        # Train BPE tokenizer
+        bpe = ByteLevelBPETokenizer()
+
+        # Set up the entire range of possible tokens as the initial alphabet
+        alphabet = [chr(i) for i in range(max_token - min_token + 1)]
+        trainer = BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=2,
+            show_progress=True,
+            special_tokens=[],
+            initial_alphabet=alphabet,
+            max_token_length=10000,
+        )
+
+        # Train the inner tokenizer (don't use ByteLevelBPETokenizer.train_from_iterator()
+        # because it doesn't support custom alphabets)
+        bpe._tokenizer.train_from_iterator(_token_iter(), trainer=trainer)
+
+        return cls(
+            PreTrainedTokenizerFast(tokenizer_object=bpe, clean_up_tokenization_spaces=False),
+            scale=scale,
+            vocab_size=vocab_size,
+            min_token=min_token,
+            time_horizon=time_horizon,
+            action_dim=action_dim,
+        )

pi_behavior_100t_meta_ft_4task/assets/IliaLarchenko/behavior_224_rgb/fast_tokenizer/processor_config.json

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+{
+  "action_dim": 22,
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "min_token": -55,
+  "processor_class": "UniversalActionProcessor",
+  "scale": 10.0,
+  "time_horizon": 30,
+  "vocab_size": 1024
+}

pi_behavior_100t_meta_ft_4task/assets/IliaLarchenko/behavior_224_rgb/fast_tokenizer/special_tokens_map.json

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+{}

pi_behavior_100t_meta_ft_4task/assets/IliaLarchenko/behavior_224_rgb/fast_tokenizer/tokenizer_config.json

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+{
+  "added_tokens_decoder": {},
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "clean_up_tokenization_spaces": false,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "processor_class": "UniversalActionProcessor",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

pi_behavior_100t_meta_ft_4task/assets/IliaLarchenko/behavior_224_rgb/norm_stats.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee2e1a97361106f87a5bece22936e02e777089bd2e8b6d4742da39dd27742328
+size 18009211

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