A $500 RL fine-tune of a 9B open model beat frontier models on catalog review

JSR_FDED

I like the 2x2 grid that describes when to fine-tune a model, when to use a frontier model, etc. From the article it’s not clear how the scorer grades every episode - was it a frontier model that assigned the grade? How does that continue to work as the model that is being fine-tuned becomes better at the task than the frontier model?

himata4113

What I really started to notice is that SOTA models are really good at putting themselves out of the job. We can see this already with GPT how luna can do 90% of what sol is used for. The only reason why china still bothers 'distilling' models is accurate training data generation, something that oai and anthropic had to spend years collecting while trying to dodge legal challenges. The more intelligent models get, the more people will offramp to cheaper solutions that get the job done. There's no real benefit to using a sota model when the accuracy is already 99% and I think that is the biggest danger to US labs.

nzeid

I didn't read the Ramp article but this reads like a post hoc fallacy. Companies with 2x revenue have money to spend on AI. Companies with 1.15x revenue don't.

sudo_cowsay

What benchmark is it? Is it super niche?

_345

"87.3% Share of the maximum achievable score our GRPO-trained 9B open-source model reached on catalog review, vs 76.9% for the best frontier configuration: a 13.5% relative improvement over the frontier, and 36% over its own untrained base (64.2%). The five frontier models, even with optimized prompts, plateaued within a tenth of a point of each other; the trained specialist cleared that ceiling." _______ This is hard for me to believe. I have a lot of skepticism that frontier models like GPT 5.5 that are likely 2T+ parameters in size only got about 12% more accurate than an untrained 9b parameter LLM.

heresalexandria

This continuous cycle of fine-tuned open models beating frontier on (often vaguely labeled/defined) benchmarks doesn't provide an accurate comparison to the expanding generalized capabilities of the SoTA, which makes them effectively meaningless. If we were to take these at face value, why is it that the frontier labs' models are making legitimate new discoveries (e.g. Erdős and Jacobian conjectures) and these models are not? To me, a better signal of capability would be similarly performing novel work at the same or better level, which they presently are not. I say this as someone who very much looks forward to open models being more capable, but to deny the gap is misguided hopeful hype.

cmiles8

The point that the major labs don’t seem to get is that the vast majority of use cases simply don’t need models that have 50 PhDs and can speak 12 languages. Most use cases are defined within constraints where costs matter a lot. As open weight models and cheap fine tuning services become the norm the whole economic framework of these mega models the labs are in an arms race building just completely crumbles. As does the economic picture that justified the massive infrastructure building that’s now broadly funded by a complex network of debt. This is what makes open weight models so threatening to them. The political and “it’s China” angle is mostly just a cover for the real reasons why they’re freaked out. The fact that models are now a pure commodity is bad enough for the big labs. If small open weight models become the norm the big labs are toast.

nothrowaways

Tldr: we don't know what we are doing like the rest of 99% AI teams.

mips_avatar

The problem i've had with finetuning models is that most of the time better prompting beats finetuning

brainless

I want small models to win and I am constantly experimenting with them. I have never tried fine-tuning and do not have that kind of budget. My approach is to remove some of the burden from models and bring into the agent. Tool calling is an example - in some tasks RAG works really well, including coding agents where code, git log, Epics/Tasks, dependencies sources, etc. are all available in very structured manner. You can save many extra tool calls if you can run separate prompts and retrieve the source data needed for the actual work - rather its prompt. And I really want to focus on search - this is the key technology if we want to use RAG instead of fine-tuning. If we can present really contextual sources in the prompts using a hybrid search approach - you can see how easily we get better results - either decisions or summaries from even small models.