Hacker News Reader

Comment by petcat | Hacker News Reader

> For constraint satisfaction, what ultimately matters is the model’s final output, since the internal process is discarded. By not requiring every intermediate step to satisfy the constraints, we give the model more freedom to find high-quality solutions that are still feasible in the end.

My (maybe naive) question is if we only check the final result then isn't it already too late and possibly the safety rules have already been irreversibly violated? It gives the example of a robot arm avoiding obstacles while still finding the shortest path, but if we only check the correctness at the end, then isn't it possible that it already collided with an obstacle?

View on HN

> but if we only check the correctness at the end, then isn't it possible that it already collided with an obstacle?

You would put the check before it actually does the thing. It's at the "end" of the process of figuring out what it wants to do, not the end of fulfilling the request or prompt

Outcome reward vs process reward models. The second is obviously better.. like getting partial credit on a physics test for wrong answers but correct method. Research is gradually hybridizing them but historically we avoided doing it the right way because of practical difficulties (labels required, more expensive and difficult) and more ideological ones (believers in magical machine intuition think it sounds too classical / logic based to be useful, pin their hopes on unproven faith in grokking at scale).

I think you are thinking of the wrong 'end'. It isn't talking about the end of the entire movement path, we are talking about the end of the LLMs decision making process, and the output (whether that is the full path the arm should take, or just a subset of the path) is checked against the requirements.

Basically, anything that is leaving the LLM is checked, rather than the internal LLM reasoning process.

I’m pretty sure this is just a poorly written article.

HardFlow seems to be a strategy for nudging the model in the right direction while giving it more freedom. Only applying the constraints at the end is a mischaracterization from what I can tell.

Reply on news.ycombinator.com