SCOPE-RL: Optimizing Reasoning Paths Before and After Success
Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards. This sparse anchor reliably verifies whether a trajectory succeeds but provides no direct feedback on the reasoning path that
https://arxiv.org/abs/2607.11506v2 ↗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
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
X
Xiaojian Liu
1.0
low confidence
H
Han Xu
1.0
low confidence
J
Jianqiang Xia
1.0
low confidence
Z
Zhixuan Li
1.0
low confidence
K
Ke Xu
1.0
low confidence
Y
Yiwei Dai
1.0
low confidence
X
Xinran Chen
1.0
low confidence
C
Changwo Wu
1.0
low confidence
Y
Yuchen Li
1.0
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