RENEW: Towards Learning World Models and Repairing Model Exploitation from Prefe
World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset. However, they are vulnerable to model exploitation where data coverage is thin. Prior work addr
https://arxiv.org/abs/2607.14180v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
None
How close the company’s idea is to your thesis statement
Sector
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 ↑ improving
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Correct facts used on the next screening or memo.
People
L
Logan Mondal Bhamidipaty
1.0
low confidence
M
Mykel Kochenderfer
1.0
low confidence
S
Subramanian Ramamoorthy
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
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· Active
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founders not scraped yet
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· Acquired
We help ML teams improve their models by improving their datasets
Peter Gao
Quinn Johnson
Ben Firshman
Andreas Jansson