The author proposes 'fork-and-flush' as a way out of those persistent idea basins.
As autoresearch agents run longer, the author asks whether they keep exploring or mostly refine approaches found early. They claim runs settle into different 'idea basins' and stay there, and introduce 'fork-and-flush' as a proposed way out.
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@menhguinRT @jackcai1206: As autoresearch agents run longer, do they keep exploring new approaches, or mostly refine the ones they found early? Turn…
@DimitrisPapailFork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of interest goes down, as a function of ideas/experiments, where the agents get pulled towards one idea or another. But we've seen very often an agent develop this weird tunnel vision, and honing in just one type of "idea", kinda like GD stuck in a local minimum. We studied this and seems we can fix it with Fork-and-Flush, a super simple and kinda cute idea. Every so often, fork the agent into T copies in the same workspace but empty the context, let them run for a bit, and continue from the best one. If you like the GD analogy, it's basically basin hopping https://en.wikipedia.org/wiki/Basin-hopping. BTW we found a silly way of also plotting "idea basins": an LLM rewrites every experiment as a spec and answers a few thousand binary queries "is Experiment A closer to B or to C?". Then we embed this NXN matrix into 2D and indeed see in many cases that each run saturates into a small region and keeps polishing it. We call these idea basins! At equal compute Fork and Flush beats single runs and best-of-N on 12/13 long-horizon tasks (some 4+ days), and in my own research projects it's been working really well! BTW we only use it at test time, but it could also be part of training or RL. Led by @jackcai1206 @christos_ziakas during Jack's internships with us, along with @vkontonis @ShivamGarg91462 @Tea_Pearce Sid Sen and Akshay Krishnamurthy.
@rbhar90RT @DimitrisPapail: Fork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of int…@menhguinRT @jackcai1206: As autoresearch agents run longer, do they keep exploring new approaches, or mostly refine the ones they found early? Turn…@DimitrisPapailFork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of interest goes down, as a function of ideas/experiments, where the agents get pulled towards one idea or another. But we've seen very often an agent develop this weird tunnel vision, and honing in just one type of "idea", kinda like GD stuck in a local minimum. We studied this and seems we can fix it with Fork-and-Flush, a super simple and kinda cute idea. Every so often, fork the agent into T copies in the same workspace but empty the context, let them run for a bit, and continue from the best one. If you like the GD analogy, it's basically basin hopping https://en.wikipedia.org/wiki/Basin-hopping. BTW we found a silly way of also plotting "idea basins": an LLM rewrites every experiment as a spec and answers a few thousand binary queries "is Experiment A closer to B or to C?". Then we embed this NXN matrix into 2D and indeed see in many cases that each run saturates into a small region and keeps polishing it. We call these idea basins! At equal compute Fork and Flush beats single runs and best-of-N on 12/13 long-horizon tasks (some 4+ days), and in my own research projects it's been working really well! BTW we only use it at test time, but it could also be part of training or RL. Led by @jackcai1206 @christos_ziakas during Jack's internships with us, along with @vkontonis @ShivamGarg91462 @Tea_Pearce Sid Sen and Akshay Krishnamurthy.@rbhar90RT @DimitrisPapail: Fork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of int…@menhguinRT @jackcai1206: As autoresearch agents run longer, do they keep exploring new approaches, or mostly refine the ones they found early? Turn…@DimitrisPapailFork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of interest goes down, as a function of ideas/experiments, where the agents get pulled towards one idea or another. But we've seen very often an agent develop this weird tunnel vision, and honing in just one type of "idea", kinda like GD stuck in a local minimum. We studied this and seems we can fix it with Fork-and-Flush, a super simple and kinda cute idea. Every so often, fork the agent into T copies in the same workspace but empty the context, let them run for a bit, and continue from the best one. If you like the GD analogy, it's basically basin hopping https://en.wikipedia.org/wiki/Basin-hopping. BTW we found a silly way of also plotting "idea basins": an LLM rewrites every experiment as a spec and answers a few thousand binary queries "is Experiment A closer to B or to C?". Then we embed this NXN matrix into 2D and indeed see in many cases that each run saturates into a small region and keeps polishing it. We call these idea basins! At equal compute Fork and Flush beats single runs and best-of-N on 12/13 long-horizon tasks (some 4+ days), and in my own research projects it's been working really well! BTW we only use it at test time, but it could also be part of training or RL. Led by @jackcai1206 @christos_ziakas during Jack's internships with us, along with @vkontonis @ShivamGarg91462 @Tea_Pearce Sid Sen and Akshay Krishnamurthy.@rbhar90RT @DimitrisPapail: Fork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of int…@menhguinRT @jackcai1206: As autoresearch agents run longer, do they keep exploring new approaches, or mostly refine the ones they found early? Turn…@DimitrisPapailFork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of interest goes down, as a function of ideas/experiments, where the agents get pulled towards one idea or another. But we've seen very often an agent develop this weird tunnel vision, and honing in just one type of "idea", kinda like GD stuck in a local minimum. We studied this and seems we can fix it with Fork-and-Flush, a super simple and kinda cute idea. Every so often, fork the agent into T copies in the same workspace but empty the context, let them run for a bit, and continue from the best one. If you like the GD analogy, it's basically basin hopping https://en.wikipedia.org/wiki/Basin-hopping. BTW we found a silly way of also plotting "idea basins": an LLM rewrites every experiment as a spec and answers a few thousand binary queries "is Experiment A closer to B or to C?". Then we embed this NXN matrix into 2D and indeed see in many cases that each run saturates into a small region and keeps polishing it. We call these idea basins! At equal compute Fork and Flush beats single runs and best-of-N on 12/13 long-horizon tasks (some 4+ days), and in my own research projects it's been working really well! BTW we only use it at test time, but it could also be part of training or RL. Led by @jackcai1206 @christos_ziakas during Jack's internships with us, along with @vkontonis @ShivamGarg91462 @Tea_Pearce Sid Sen and Akshay Krishnamurthy.@rbhar90RT @DimitrisPapail: Fork-and-Flush: help your agents escape Idea Basins!! I love thinking of AutoResearch as Idea Descent: a metric of int…—
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