# PLAN

## Goal
Train a 50k-step IMF baseline with the original ResNet vision backbone (no full-AttnRes vision replacement), using only `top` and `front` cameras as image conditioning.

## Fixed comparison contract
- Agent: `resnet_imf_attnres`
- Vision backbone mode: `resnet`
- `pred_horizon=16`
- `num_action_steps=8`
- `n_emb=384`, `n_layer=12`, `n_head=1`, `n_kv_head=1`
- `inference_steps=1`
- `batch_size=80`, `lr=2.5e-4`, cosine scheduler, warmup 2000
- dataset: `/home/droid/project/diana_sim/sim_transfer`
- cameras: `[top, front]` only
- training budget: `max_steps=50000`
- rollout validation: every 5 epochs, 5 episodes, headless

## Resource plan
- Host: local
- GPU: RTX 5090 (GPU 0)

## Execution path
1. Run a short 2-step smoke test on GPU with the exact 2-camera config.
2. If smoke passes, launch the 50k main run with durable log redirection.
3. Record run name, pid, log path, and SwanLab URL into suite status.

## Fallbacks
- If batch 80 OOMs, fall back to batch 64 with scaled lr 2.0e-4.
- If dataloader startup is unstable, reduce num_workers from 12 to 8.