Research map / Large language models / Alignment and preferences
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use hindsight instruction relabeling rather than FARL's correct-answer-only filtering | FLAN-T5-large · Tracking Shuffled Objects (5) | accuracy: 15.6 -> 61.2 | Helped |
| Use hindsight instruction relabeling rather than FARL's correct-answer-only filtering | FLAN-T5-large · Logical Deduction (5 Objects) | accuracy: 54.0 -> 67.0 | Helped |
| Use hindsight instruction relabeling rather than FARL's correct-answer-only filtering | FLAN-T5-large · Word Sorting | accuracy: 3.4 -> 3.4 | No clear effect |
| Add sub-output sampling (ablation) | HIR · Geometric Shapes; Tracking Shuffled Objects (3); Logical Deduction (3 Objects) | accuracy: 86.1 -> 90.3; 100.0 -> 100.0; 75.0 -> 91.7 | Helped |
| Add entropy regularization (ablation) | HIR · Geometric Shapes; Tracking Shuffled Objects (3); Logical Deduction (3 Objects) | accuracy: 47.2 -> 90.3; 100.0 -> 100.0; 48.3 -> 91.7 | Helped |
| Add label smoothing (ablation) | HIR · Geometric Shapes; Tracking Shuffled Objects (3); Logical Deduction (3 Objects) | accuracy: 84.7 -> 90.3; 100.0 -> 100.0; 23.3 -> 91.7 | Helped |
Closest papers by meaning
- Checklists Are Better Than Reward Models For Aligning Language Models (NeurIPS 2025 · similarity 0.90)
- Chain of Hindsight aligns Language Models with Feedback (ICLR 2024 · similarity 0.90)
- ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models (ICML 2024 · similarity 0.89)
- BLEUBERI: BLEU is a surprisingly effective reward for instruction following (NeurIPS 2025 · similarity 0.89)
- Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models (NeurIPS 2025 · similarity 0.89)
- Generalizing Verifiable Instruction Following (NeurIPS 2025 · similarity 0.89)
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