ICLR 2026 - Submissions

SubmissionsReviews

Submissions

Summary Statistics

Quantity AI Content Count Avg Rating
0-10% 1 (100%) 4.00
10-30% 0 (0%) N/A
30-50% 0 (0%) N/A
50-70% 0 (0%) N/A
70-90% 0 (0%) N/A
90-100% 0 (0%) N/A
Total 1 (100%) 4.00
Title Abstract Avg Rating Quantity AI Content Reviews Pangram Dashboard
Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning Adversarial imitation learning (AIL) achieves superior expert sample efficiency compared to behavioral cloning (BC) but requires extensive online environment interactions. Recent empirical works have ... 4.00 3% See Reviews View AI Dashboard
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