Adaptive Decoding via Test-Time Policy Learning for Self-Improving Generation
Decoding strategies largely determine the quality of Large Language Model (LLM) outputs, yet widely used heuristics such as greedy or fixed temperature/top-p decoding are static and often task-agnostic, leading to suboptimal or inconsistent generation quality across domains that demand stylistic or structural flexibility. We introduce a reinforcement learning-based decoder sampler that treats decoding as sequential decision-making and learns a lightweight policy to adjust sampling parameters at test-time while keeping LLM weights frozen. We evaluated summarization datasets including BookSum, arXiv, and WikiHow using Granite-3.3-2B and Qwen-2.5-0.5B. Our policy sampler consistently outperf...
| Authors | Asmita Bhardwaj, Yuya Jeremy Ong, Eelaaf Zahid et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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