Papers with constraint decomposition strategies
Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following (2026.acl-long)
Copied to clipboard
| Challenge: | Existing reinforcement learning approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks. |
| Approach: | They propose a self-supervised reinforcement learning framework that eliminates dependency on external supervision by deriving reward signals directly from instructions and generating pseudo-labels for reward model training. |
| Outcome: | The proposed framework achieves strong improvements across 3 in-domain and 5 out-of-domain datasets while maintaining computational efficiency. |