I’m morbidly curious whether the (supposedly) superior compaction support in recent GPT models with an appropriate harness has anything to do with this. A conventional LLM with conventional attention is, of course, wildly unsuitable to continuous tasks like driving, but maybe as the technology advances it will improve in its ability to sort-of work.
Not unless you want to cheat the attention mechanism or do extra computations running prefill in a front-truncated version of the conversation.
Also, to the extent that the model reasons and thus learns something, if you blindly truncate the front, you will lose that knowledge. In the OP, the LLM that actually navigated the course successfully only did it on the second try. It it forgot the first failed try, it might not have succeeded :)
amluto · · focus · HN ↗
torginus · · focus · HN ↗
amluto · · focus · HN ↗
Also, to the extent that the model reasons and thus learns something, if you blindly truncate the front, you will lose that knowledge. In the OP, the LLM that actually navigated the course successfully only did it on the second try. It it forgot the first failed try, it might not have succeeded :)