Post-Training Shifts Confidence: A Three-Stage Analysis of How SFT, RL, and OPD
Large language models have made strong reasoning gains through supervised fine-tuning, reinforcement learning, and on-policy distillation, yet these post-training methods are usually evaluated only by final-answer accuracy. We study how the
https://arxiv.org/abs/2607.13753v1 ↗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
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
S
Shuhao Li
1.0
low confidence
G
Guodong Du
1.0
low confidence
A
Anhao Zhao
1.0
low confidence
W
Wanyu Lin
1.0
low confidence
T
Tianyu Yuan
1.0
low confidence
X
Xiaoyu Shen
1.0
low confidence