|
|
|
|
|
|
by pitched
11 days ago
|
|
|
Is this lazy version of RLM where we use subagents and get them to output markdown documents? With one root agent coordinating, it can create a reasonable prompt and provide paths to the markdown documents for the next agent. This keeps context really low because each subagent is filtering the context needed for the next by bouncing it through that document. Proper RLM looks like you’re allowing agents to directly modify their own context though, like closing browser tabs they don’t need anymore. I haven’t seen anyone actually doing this though. |
|
|
If not, my agent-level chains just look like:
'''
Turn 1: OK, my task is X, so I should grep for it. Oh, it produced these results:
(Message pairs)
I should expand the context around those message pairs that look relevant.
(3 message pairs around search result)
I should save 1 of these, as it contains relevant information.
[Enforce Tool call limit]
[Delete all context added, except the search tool used already, and the relevant result(s) found.]
Turn 2: OK, my task is this, and it seems I already have this result, but I still need...
...
Turn 10: OK, after that search, my answer is:
[Response]
'''
I've never bothered to let agents self-remove from context, so I would guess it's 'lazy' in that sense. It seems more complex than the task requires in this case, though I can see the benefits on more complex tasks. If you're already saying "this is relevant info", I figure it's simplest to just enforce deletion of everything not marked relevant. In chained prompts, when you're trying to keep costs low and use weaker models, it seems best to limit decision-making as much as possible to make the models as deterministic as possible (on really dumb tasks like, "What is the colour of this goblin's hair?").
There are likely other parts of the actual paper's implementation where the ways I'm implementing it are lazy (because I'm doing this stuff for artistic/fucking around reasons, rather than to advance the field, or implement perfectly), and I think there are various interpretations of what "RLM" should mean. But I found the original paper very helpful, with lots of interesting ideas in, and think it's one where people can take what they need from.