Adaptive Decoding via Test-Time Policy Learning for Self-Improving Generation
arXiv:2603.18428v1 Announce Type: new Abstract: 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 outperforms greedy and static baselines, achieving relative gains of up to +88% (BookSum, Granite) and +79% (WikiHow, Qwen). Reward ablations show that overlap-only objectives underperform compared to composite rewards, while structured shaping terms (length, c
arXiv:2603.18428v1 Announce Type: new Abstract: 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 outperforms greedy and static baselines, achieving relative gains of up to +88% (BookSum, Granite) and +79% (WikiHow, Qwen). Reward ablations show that overlap-only objectives underperform compared to composite rewards, while structured shaping terms (length, coverage, repetition, completeness) enable stable and sustained improvements. These findings highlight reinforcement learning as a practical mechanism for test-time adaptation in decoding, enabling domain-aware and user-controllable generation without retraining large models.
Executive Summary
This article presents a novel approach to Large Language Model (LLM) decoding by introducing a reinforcement learning-based decoder sampler. The proposed method learns a lightweight policy to adjust sampling parameters at test-time, enabling domain-aware and user-controllable generation without retraining large models. The authors evaluate their approach on summarization datasets using two LLMs and demonstrate significant improvements over static baselines, with relative gains of up to +88% and +79%. The study highlights the potential of reinforcement learning as a practical mechanism for test-time adaptation in decoding. However, the article also acknowledges the limitations of their approach, including the need for reward engineering and the potential for overfitting.
Key Points
- ▸ The article proposes a reinforcement learning-based decoder sampler for LLMs.
- ▸ The sampler learns a lightweight policy to adjust sampling parameters at test-time.
- ▸ The approach enables domain-aware and user-controllable generation without retraining large models.
- ▸ The authors evaluate their approach on summarization datasets with significant improvements over static baselines.
Merits
Strength in Adaptability
The proposed method enables domain-aware and user-controllable generation by adjusting sampling parameters at test-time.
Improved Performance
The approach achieves significant improvements over static baselines, with relative gains of up to +88% and +79%.
Demerits
Reward Engineering Limitations
The article acknowledges the need for reward engineering to achieve optimal performance.
Potential for Overfitting
The authors note that the approach may be prone to overfitting, particularly when using complex reward functions.
Expert Commentary
The article presents a novel and innovative approach to LLM decoding, which has the potential to improve the performance of these models in various applications. The proposed method is particularly well-suited for domains that demand stylistic or structural flexibility. However, the article also highlights the need for reward engineering and the potential for overfitting, which requires careful consideration in future work. The study demonstrates the potential of reinforcement learning as a practical mechanism for test-time adaptation in decoding, which can inform policy decisions in AI development.
Recommendations
- ✓ Future research should focus on developing more robust reward functions that can handle complex tasks and domains.
- ✓ The proposed approach should be evaluated on a wider range of tasks and domains to demonstrate its generalizability and applicability.
