Enhancing the reliability of natively unstable Physically Un- clonable Functions (PUFs) is a major requirement when the PUF is to generate secret identifiers like cryptographic keys. One traditional method is to rely on an addition of a public word: the Helper Data. However, it involves extra complexity and constitutes a vulnerability against attacks manipulating it. In this work, we show that for PUFs based on oscillations, such as Loop-PUFs (LPUF) can simultaneously increase the stability of the PUFs responses and reduce the required amount of helper data to decrease the complexity and increase the security. We proceed in two steps: First, we improve the reliability of the LPUF using dynamically determined repeated measurements and decision process. The number of repetitions per challenge is automatically tuned according to its reliability level and measurement window. Second, we investigate lightweight helper data (less than one byte). Experimental validation of our approach is carried out on 640 LPUFs to characterize the PUF reliability under different temperatures. This provides the assessment of the probability that a given Key Error Rate (KER) is achieved. This, in turn, yields the probability that there is an oscillator with arbitrarily low KER among any given number of oscillators. Performances remain notably stable when subject to increasing temperature.