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Paper Rebuttal

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2025 Nov 12 11:8
Editor
Editor
Seonglae ChoSeonglae Cho
Edited
Edited
2025 Nov 28 19:14
Refs
Refs
Paper Review
  • Rebuttal by Authors
  • Official Comment by Authors
how
  • peer pressure
  • small table
  • followup reminder
    • We have addressed all reviewer concerns and added new experiments that further validate our conclusions.
 

examples

neurips
Quantifying Elicitation of Latent Capabilities in Language Models
Large language models often possess latent capabilities that lie dormant unless explicitly elicited, or surfaced, through fine-tuning or prompt engineering. Predicting, assessing, and understanding...
Quantifying Elicitation of Latent Capabilities in Language Models
https://openreview.net/forum?id=Dkgx2pS4Ww
tmlr
Open Problems in Mechanistic Interpretability
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in...
Open Problems in Mechanistic Interpretability
https://openreview.net/forum?id=91H76m9Z94
iclr
MaxInfoRL: Boosting exploration in reinforcement learning through...
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected...
MaxInfoRL: Boosting exploration in reinforcement learning through...
https://openreview.net/forum?id=R4q3cY3kQf
 
 

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Paper Rebuttal
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