Questionare about Post-training stage at tables you mentioned

[karu-veh]

Thanks for sharing you guys insight to github.
I wonder what is different each stage about "each post-training" at below table rows you mentioned

Can you explain it more in detail?
Especially I wonder all the "post-training" you mentioned below are RL-based finetune in close loop environment.
Or post trainin like "Post-training on common logs" is just trainining with open loop reward function like collision score.

[WCJ-BERT]

Thanks for the great question!

To clarify: all post-training rows in Extended Data Table 1 (except the SFT row) are closed-loop RL, including "post-training on common logs". The difference between rows is not open-loop vs. closed-loop, but rather the source of scenarios and the Other agents behaviour model. The reward structure is the same across all configurations: a hard-constraint gate for collision / drivable-area compliance multiplied by a weighted average of soft objectives (progress, TTC, comfort).

Here is what each row means:

Supervised fine-tuning on rare logs — the only non-RL row. Standard imitation learning on rare-event logs, no reward signal. Raw data.

Post-training on common logs — closed-loop RL, but scenarios are drawn from ordinary driving logs (not long-tail), raw data.

Post-training on rare logs — closed-loop RL on failure-prone scenarios discovered from real logs, raw data.

Post-training on rare synthetic replays — RL on rare scenarios where other agents' behaviour is faithfully replayed from logs (non-reactive), synthetic data (3DGS).

Post-training on rare rollouts w/o Behaviour WM — RL on rare scenarios which are feasible but failed. Synthetic data (3DGS) + raw data.

Post-training with World Engine (full) — builds on the above by adding the Behaviour World Model, which generates diverse counterfactual traffic variations via goal conditioning and optimization guidance. Synthetic data (3DGS) + raw data.

Besides, once the arxiv is ready, you may find more details in our preprint paper. Coming soon.