Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rew
While reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capabilities of large language models (LLMs), the generalizability of the resulting models remains poorly understood. In this work, we estab
https://arxiv.org/abs/2607.14506v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
How close the company’s idea is to your thesis statement
Sector
llm, ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
Y
Yuxuan Zhu
2.0
low confidence
R
Rohan Alur
2.2
low confidence
D
Daniel Kang
2.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Unsloth AI
· Active
Open-Source Reinforcement Learning (RL) & Fine-tuning for LLMs.
founders not scraped yet