Papers with entropy regularization methods
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing (2020.acl-main)
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| Challenge: | Prior work has explored regularizing the output distributions of probabilistic models to alleviate overfitting. |
| Approach: | They propose a family of entropy regularizers that have a connection to regularization . they find that label smoothing provably does not allow for sparsity in an output distribution . |
| Outcome: | The proposed method improves the relationship between model entropy and performance on language generation tasks. |
Token-Level Policy Optimization: Linking Group-Level Rewards to Token-Level Aggregation via sequence-level likelihood (2026.acl-long)
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| Challenge: | Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs). |
| Approach: | They propose a token-level framework that leverages sequence-level likelihood to link group-level rewards with individual tokens via token- level aggregation and introduces a KL-Divergence mask constraint that targets tokens with positive advantages and decreasing entropy to mitigate abrupt policy updates. |
| Outcome: | Experiments show that TEPO achieves state-of-the-art performance on mathematical reasoning benchmarks and reduces convergence time by 50% compared with GRPO/DAPO. |