Event Horizon Labs (YC W24) Is Hiring (ycombinator.com)

Hey HN, we’re Andrew and Derik at RunRL (https://runrl.com/). We've built a platform to improve models and agents with reinforcement learning. If you can define a metric, we'll make your model or agent better, without you having to think about managing GPU clusters.

Here's a demo video: https://youtu.be/EtiBjs4jfCg

I (Andrew) was doing a PhD in reinforcement learning on language models, and everyone kept...not using RL because it was too hard to get running. At some point I realized that someone's got to sit down and actually write a good platform for running RL experiments.

Once this happened, people started using it for antiviral design, formal verification, browser agents, and a bunch of other cool applications, so we decided to make a startup out of it.

How it works:

- Choose an open-weight base model (weights are necessary for RL updates; Qwen3-4B-Instruct-2507 is a good starting point)

- Upload a set of initial prompts ("Generate an antiviral targeting Sars-CoV-2 protease", "Prove this theorem", "What's the average summer high in Windhoek?")

- Define a reward function, using Python, an LLM-as-a-judge, or both

- For complex settings, you can define an entire multi-turn environment

- Watch the reward go up!

For most well-defined problems, a small open model + RunRL outperforms frontier models. (For instance, we've seen Qwen-3B do better than Claude 4.1 Opus on antiviral design.) This is because LLM intelligence is notoriously "spiky"; often models are decent-but-not-great at common-sense knowledge, are randomly good at a few domains, but make mistakes on lots of other tasks. RunRL creates spikes precisely on the tasks where you need them.

Pricing: $80/node-hour. Most models up to 14B parameters fit on one node (0.6-1.2 TB of VRAM). We do full fine-tuning, at the cost of parameter-efficiency (with RL, people seem to care a lot about the last few percent gains in e.g. agent reliability).

Next up: continuous learning; tool use. Tool use is currently in private beta, which you can join here: https://forms.gle/D2mSmeQDVCDraPQg8

We'd love to hear any thoughts, questions, or positive or negative reinforcement!

Comments (10)

ripbozo · 58m ago

Was excited to see something about reinforcement learning as I'm working on training an agent to play a game, but apparently all reinforcement learning nowadays is for LLMs.

-_- · 6m ago

Have you heard of https://puffer.ai? Might fit your use case

ag8 · 53m ago

Yeah, for better or worse, the way the median startup interfaces with AI these days is through an LLM API, and that's what all the workflows are built around, so that's what we're targeting. Though, depending on what you're trying to do, I wouldn't discount the use of starting with a pretrained model—there was that famous result from 2022 that showed that pretraining a model on _Wikipedia_ made training on Atari games more than twice as efficient [0]; these days, LLMs have huge amounts of priors about the real world that make them great starting points for a surprisingly diverse set of tasks (e.g. see the chemistry example in our video!)

[0]: https://arxiv.org/abs/2201.12122

nextworddev · 2h ago

Is there any credence to the view that these startups are basically dspy wrappers

-_- · 2h ago

DSPy is great for prompt optimization but not so much for RL fine-tuning (their support is "extremely EXPERIMENTAL"). The nice thing about RL is that the exact prompts don't matter so much. You don't need to spell out every edge case, since the model will get an intuition for how to do its job well via the training process.

nextworddev · 1h ago

Isn’t the latest trend in RL mostly about prompt optimization as opposed to full fine tuning

ag8 · 1h ago

prompt optimization is very cool, and we use it for certain problems! The main goal with this launch is to democratize access to "the real thing"; in many cases, full RL allows you to get the last few percent in reliability for things like complex agentic workflows where prompt optimization doesn't quite get you far enough.

There's also lots of interesting possibilities such as RLing a model on a bunch of environments and then prompt optimizing it on each specific one, which seems way better than, like, training and hot-swapping many LoRAs. In any case, _someone's_ ought to provide a full RL api, and we're here to do that well!

nextworddev · 1h ago

Thanks. Is this mainly for verifiable tasks or any general task

ag8 · 1h ago

It's for any task that has an "eval", which is often verifiable tasks or ones that can be judged by LLMs (e.g. see [0]). There's also been recent work such as BRPO [1] and similar approaches to make more and more "non-verifiable" tasks have verifiable rewards!

[0]: https://runrl.com/blog/funniest-joke

[1]: https://arxiv.org/abs/2506.00103

-_- · 1h ago

There needs to be some way of automatically assessing performance on the task, though this could be with a Python function or another LLM as a judge (or a combination!)