Comment by veselin
2 days ago
I run evals and the Todo tool doesn't help most of the time. Usually models on high thinking would maintain Todo/state in their thinking tokens. What Todo helps is for cases like Anthropic models to run more parallel tool calls. If there is a Todo list call, then some of the actions after are more efficient.
What you need to do is to match the distribution of how the models were RL-ed. So you are right to say that "do X in 200 lines" is a very small part of the job to be done.
Curious what kinds of evals you focus on?
We're finding investigating to be same-but-different to coding. Probably the most close to ours that has a bigger evals community is AI SRE tasks.
Agreed wrt all these things being contextual. The LLM needs to decide whether to trigger tools like self-planning and todo lists, and as the talk gives examples of, which kind of strategies to use with them.
I am taking for SWE bench style problems where Todo doesn't help, except for more parallelism.
Was guessing that, coding tasks are a valuable but myopic lense :)
I'm guessing a self-updating plan there is sufficient. I'm not actually convinced today's current plan <> todolist flow makes sense - in the linked PLAN.md, it gets unified, and that's how we do ai coding. I don't have evals on this, but from a year of vibes coding/engineering, that's what we experientially reached across frontier coding models & tools. Nowadays we're mixing in evals too, but that's a more complicated story.