This is my research area. I just finished reviewing six NeurIPS papers (myself, no LLM involved) on LLM Agents for discovery and generation and I'm finding that evaluating LLM agents on raw performance for a task isn't as insightful anymore -- every paper is claiming state of the art 10x performance boost by {insert random acronym that devolves into combinatorial search}. Rather the true test for such algorithms is whether the empirical scaling curves for these algorithms are more computationally amenable than an existing baseline search algorithm (like CoT).
Three motivating points:
- GEPA / evolutionary agents are performing a zero-th order (no gradient) optimization in a combinatorial space. Their loss curves are VERY noisy and stochastic. If we run such agents multiple times, the performance variance is extremely high -- and in some cases cancels out the gains from single experiment. However, obtaining the error bounds is hard because the API costs are pretty restrictive.
- The problem we face with test time scaling is not that prompt engineering is ineffective/less effective than fine-tuning. It is that fine-tuning _reliably_ increases performance for a model for any subset of tasks and the scaling curves for performance per additional data token are well understood.
- Test time optimization techniques work well on in-distribution problems (e.g. generate and debug this Python code) but fail pretty badly on even slightly out of distribution problems (e.g. generate and debug this Julia code). Compare this to gradient search -- it wouldve been so fascinating and confusing if SGD failed to optimize a CNN image classifier on COCO but worked very well on ImageNet.
How do people feel about this? Does this line up with your viewpoints?
bironran · 3h ago
To me that's a complete circle back to lessons learned from neural networks - I long suspected we'd be heading that way again with transformers being a computational step rather than the whole thing.
strangescript · 3h ago
Is the name meant to be a jab at you know who or am I reading too much into it?
kennyadam · 2h ago
Why would they want to take a jab at Francis Kojo Kwarteng Arthur (Esq), the CEO of Ghana Export Promotion Authority?
barrenko · 5h ago
These models / nets / whatever are much "smarter" (loaded term) than we think, we just don't know how to plug-in properly yet.
"We are not interested in the fact that the brain has the consistency of cold porridge."
— Alan Turing
codekilla · 4h ago
I guess Turing couldn’t see the trillions of base pairs of DNA, complex methylation states, dendritic spines of the neurons, etc., just for starters.
quantumHazer · 4h ago
This is not how science and engineering work and an arxiv should not be taken at face value.
hnuser123456 · 4h ago
It has been commonly observed that the current crop of LLMs can be too agreeable/sycophantic (or on some topics, too disagreeable) due to the commonly chosen RLHF priorities.
Simply asking the LLM in two separate contexts the same question but from opposing perspectives, then in a third context asking it to analyze both responses and choose the most neutral and objective take, you wipe out any "(dis)agreeableness" bias and dig closer to a deeper, more nuanced synthesis of a given topic. This paper is just taking this idea to the next level.
This isn't really possible with RLHF alone unless you train the LLM to often give two opposing perspectives, which would get tiring.
underlines · 4h ago
Looking at a Problem from various perspectives, even posing ideas, is exactly what reasoning models seem to simulate in their thinking CoT to explore the solution space with optimizations like MCMC etc.
ACCount36 · 4h ago
A sufficiently capable AI would be able to plug itself in properly too.
One more reason to be wary of pushing for better capabilities.
justanotheratom · 6h ago
anyone working on an dspy optimizer for this?
viksit · 5h ago
they’ve already written one! see omar’s x account for details!
> ArXivIQ exists to turn that fire-hose into jet-streams of insight: every paper is hand-picked by a human editor, then pushed through a purpose-built multi-agent AI pipeline that dissects methods, experiments, and limitations in minutes instead of hours.
ACCount36 · 7h ago
And then you use self-distillation to wire the improved prompts back into the LLM. Bam, free metacognitive skills.
falcor84 · 5h ago
Self-distillation generally refers to training a smaller model, right? I suppose for full metacognition you would use it fine-tune the existing model based on its older self?
ACCount36 · 4h ago
No, training a smaller model off a more capable larger model (or an ensemble of models) is the "usual" distillation.
"Self-distillation" refers to distilling from a model into a copy of itself. Which is of limited use - unless you can steer the teacher, and want the student to internalize that steering.
The reason for doing self-distillation here is that we have both access to a richer representation (logit stream), and want to capture a richer behavior - not the answers themselves, but better reasoning techniques that are downstream from better prompts.
Three motivating points:
- GEPA / evolutionary agents are performing a zero-th order (no gradient) optimization in a combinatorial space. Their loss curves are VERY noisy and stochastic. If we run such agents multiple times, the performance variance is extremely high -- and in some cases cancels out the gains from single experiment. However, obtaining the error bounds is hard because the API costs are pretty restrictive.
- The problem we face with test time scaling is not that prompt engineering is ineffective/less effective than fine-tuning. It is that fine-tuning _reliably_ increases performance for a model for any subset of tasks and the scaling curves for performance per additional data token are well understood.
- Test time optimization techniques work well on in-distribution problems (e.g. generate and debug this Python code) but fail pretty badly on even slightly out of distribution problems (e.g. generate and debug this Julia code). Compare this to gradient search -- it wouldve been so fascinating and confusing if SGD failed to optimize a CNN image classifier on COCO but worked very well on ImageNet.
How do people feel about this? Does this line up with your viewpoints?
"We are not interested in the fact that the brain has the consistency of cold porridge." — Alan Turing
Simply asking the LLM in two separate contexts the same question but from opposing perspectives, then in a third context asking it to analyze both responses and choose the most neutral and objective take, you wipe out any "(dis)agreeableness" bias and dig closer to a deeper, more nuanced synthesis of a given topic. This paper is just taking this idea to the next level.
This isn't really possible with RLHF alone unless you train the LLM to often give two opposing perspectives, which would get tiring.
One more reason to be wary of pushing for better capabilities.
1: https://arxiviq.substack.com/p/coming-soon
> ArXivIQ exists to turn that fire-hose into jet-streams of insight: every paper is hand-picked by a human editor, then pushed through a purpose-built multi-agent AI pipeline that dissects methods, experiments, and limitations in minutes instead of hours.
"Self-distillation" refers to distilling from a model into a copy of itself. Which is of limited use - unless you can steer the teacher, and want the student to internalize that steering.
The reason for doing self-distillation here is that we have both access to a richer representation (logit stream), and want to capture a richer behavior - not the answers themselves, but better reasoning techniques that are downstream from better prompts.