| --- |
| base_model: Qwen/Qwen3.5-4B |
| library_name: transformers |
| tags: |
| - vision-language-navigation |
| - r2r |
| - rxr |
| - simplememvln |
| --- |
| # SimpleMemVLN joint R2R + RxR_15deg FullContext+B |
| |
| **Not yet evaluated in Habitat.** Training loss is not a navigation success metric. |
| |
| ## Published snapshots |
| `epoch-1/` is the mid-schedule snapshot at update 3852 of one two-epoch |
| cosine run. `epoch-2/` is the completed second epoch at update 7704. |
| These are the native LLM text-output policy (option B), not candidate logits. |
| Both epochs are retained. This joint R2R + RxR_15deg model is not yet |
| evaluated in Habitat. Epoch-2 action-weighted training loss: 0.0410839. |
|
|
| ## Recipe and provenance |
| Joint R2R training data and English-guide RxR_15deg trajectories, full episodes. |
| Initialized from Qwen/Qwen3.5-4B revision |
| `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`. |
| Source base commit: `6bb2b10a2fe21cb6e25a265afc5a2af39b7de4f6`, with input-staging |
| and checkpoint-recovery patches. The tested source is identified by |
| `source.sha256`; the dataset manifest by `manifest.sha256`. |
| Serializer: `vln_append_only_chat_v1`. Native LLM text-action head (option B), |
| gold action history; per-action mean token cross-entropy including assistant |
| terminator, normalized over actions in the distributed accumulation window. |
| Frozen vision/merger, trainable text backbone and LLM head; BF16, ZeRO-2, |
| nonreentrant gradient checkpointing with long-sequence activation offload. |
| Full-context causal attention, no trajectory truncation or TBPTT. |
| Global batch 8 = four H100 GPUs × one episode/rank × GAS2. |
| Two epochs, 7704 optimization updates, 232 warmup updates, peak LR 5e-6, |
| cosine decay to 10% of peak, weight decay 0.01, seed 429. |
| Observation-before-action alignment is declared but not independently |
| verified against collector source or Habitat replay. |
| |
| ## Loading and integrity |
| Use the SimpleMemVLN wrapper loader |
| `qwen_vl.train.vln_runtime.load_checkpoint(epoch_directory, base_model_path)` |
| with the pinned base snapshot. These are navigation-wrapper weights, not a |
| plain AutoModel state dictionary. Navigation metadata records the recipe. |
| Only model, processor, tokenizer, provenance and selected training metadata |
| are published. Optimizer/RNG state, dataset images and credentials stay local. |
| `SHA256SUMS.json` records content hashes. The uploader downloads every file |
| at its immutable Hub commit and verifies SHA256 before reporting success. |
| |
