ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 0 (0%) | N/A |
| 10-30% | 0 (0%) | N/A |
| 30-50% | 1 (100%) | 4.00 |
| 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 |
|---|---|---|---|---|---|
| Forecasting-Conditioned Reinforcement Learning: Embedding Forecastability as an Inductive Bias | We introduce Forecasting-Conditioned Reinforcement Learning (FoRL), an extension to model-free Reinforcement Learning (RL) agents that augments the policy with multi-step self-forecasts. FoRL is train... | 4.00 | 37% | See Reviews | View AI Dashboard |