---
base_model: Qwen/Qwen3.5-4B
tags:
- vision-language-navigation
- simplememvln
- r2r
- rxr
- dual-lane
- no-action-history
---
# SimpleMemVLN FullContext dual-lane, no action history — base

`epoch-2/` is the completed two-epoch model at update **7704**. The
first-epoch snapshot remains available under `epoch-1/`.

Initialized directly from the original Qwen3.5-4B base weights, with fresh step lanes.

## Architecture and training

Qwen/Qwen3.5-4B revision `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a`.
FullContext attention with parallel step lanes at layers 16, 20, 24 and 28,
using the `step_end` clock. Four-way candidate logits from pretrained trainable
LM-head rows; no appended action-history tokens.
Serializer: `vln_candidate_logits_no_action_history_v1`.
Frozen vision encoder and merger, trainable language backbone and step lanes.
Complete trajectories, gradient checkpointing, CPU activation offload for long
sequences, BF16 and DeepSpeed ZeRO-2.

Joint R2R + English-guide RxR_15deg: 30,815 episodes (10,819 + 19,996).
Global batch 8 = four H100 GPUs × one episode/rank × accumulation 2.
Backbone/head LR 5e-6; step-lane LR 1e-4; warmup 232 updates;
cosine decay to 10% of peak, weight decay 0.01, seed 429.
Epoch-1 action-weighted training loss: **0.3897369707**.
Epoch-2 action-weighted training loss: **0.1094553045**.
These are training metrics; this snapshot has not been evaluated in Habitat.

## Loading and provenance

Use SimpleMemVLN source commit `fe9914d6c900b41f17df880753d651eb610d0837` and the pinned runtime documented
in the `streaming_logits_dual` branch. Download the epoch directory and use:

```python
from qwen_vl.train.vln_runtime import load_checkpoint
model, serializer = load_checkpoint("downloaded_repo/epoch-2", pinned_base_model_path)
```

The navigation wrapper and step-lane weights are included in `pytorch_model.bin`.
This requires the SimpleMemVLN loader and the pinned base model/processor;
an unmodified AutoModel loader does not implement this navigation contract.
`navigation.json` records the complete recipe and architecture.
Model, tokenizer/processor, epoch report and selected provenance are published.
Optimizer/RNG tensors, dataset contents and credentials remain local.
`SHA256SUMS.json` records file hashes. Every uploaded file is downloaded from
the immutable publication commit and checked against its local SHA-256.