Imagine a production pipeline where everything looks perfect on paper. Your average latency is low, your throughput is steady, and your benchmarks suggest the system is humming along efficiently. Then, you notice a phantom bottleneck. Occasionally, the entire system grinds to a halt, not because of a spike in traffic or a hardware failure, but because of a handful of requests that simply refuse to end. In the world of high-throughput AI inference, this is the nightmare of the tail latency, where a tiny minority of outliers dictates the experience for every single user.
The 2.42% Bottleneck in Qwen2.5-VL
This systemic paralysis was recently quantified by the DharmaOCR research team during the development of a domain-specific OCR model. While utilizing the Qwen2.5-VL-7B-Instruct model for PDF document processing, the team observed a phenomenon known as text degeneration. In these instances, the model fails to generate the End-of-Sequence (EOS) token, which serves as the signal for the system to stop generating text. Instead, the model enters a loop, repeating a specific phrase or token fragment indefinitely until it hits the hard limit of the system's max-token configuration.
The numbers reveal a staggering disproportion between the frequency of these errors and their impact on performance. The research found that only 2.42% of requests suffered from this text degeneration. However, these few failures acted as a massive anchor on the entire system. When the team replaced these degenerate requests with normal ones, the total wall-clock inference time plummeted from 7.3 minutes to 4.2 minutes. This means that a mere 2.42% of requests were responsible for inflating the total processing time by 42.47%.
This is not a simple case of a model giving a wrong answer. It is a resource exhaustion attack triggered by the model's own internal logic. In a batch processing environment, the system does not simply discard a failing request; it waits for the model to finish. Because the degenerate requests only end when they hit the maximum token limit, they occupy the GPU for the longest possible duration, forcing every other healthy request in that batch to wait for the slowest member to finish. Detailed analysis and demos of these findings are available via the HuggingFace space where the DharmaOCR paper is hosted.
The MLE Trap: Why Decoding Strategies Fail
To understand why Qwen2.5-VL falls into these loops, one must look past the hyperparameters and into the very foundation of how modern LLMs are trained. Almost every commercial model today relies on Maximum Likelihood Estimation (MLE) as its primary objective function. The goal of MLE is to maximize the probability of the next token given the preceding context, effectively minimizing the Negative Log-Likelihood (NLL). While this approach is brilliant for ensuring linguistic continuity and fluency, it creates a structural vulnerability in the model's probability geometry.
Because LLMs are autoregressive, they do not have a global view of the sequence they are creating; they only see the tokens generated so far. This leads to a self-reinforcement phenomenon. As noted in research by Holtzman et al (2020), when a specific token or phrase appears frequently in the recent context, the conditional probability of that token being selected again increases. Once the model enters a repetitive pattern, the probability gradient begins to point inward. The pattern itself becomes the strongest evidence for the next token, creating a feedback loop that becomes nearly impossible to break.
In this state, the probability of the EOS token—the only way for the model to exit gracefully—drops to near zero. The model is not making a random mistake; it is following the mathematical path of least resistance carved into its weights during training. This is why traditional decoding strategies like adjusting temperature, using Top-p sampling, or applying repetition penalties are insufficient. These tools are merely filters applied to the final probability distribution. They can make it less likely for a model to enter a loop, but they cannot erase the high-probability loop regions that exist within the model's internal activation geometry. Whether the model is a general-purpose giant or a domain-specific specialist, if it was trained via MLE, it inherits this structural predisposition toward degeneration.
vLLM and the Cost of Zombie Requests
The impact of this structural flaw is magnified by the way modern inference engines manage memory. The analysis focused on vLLM, which uses Paged Memory to implement dynamic batching. In this architecture, multiple requests are grouped together to maximize GPU utilization, and the memory required for each request—specifically the Key-Value (KV) cache—grows linearly with the number of tokens generated.
Under normal conditions, a request generates its answer, hits the EOS token, and immediately releases its KV cache back to the system. However, a degenerate request becomes a zombie. It refuses to die, clinging to its allocated GPU memory and occupying a slot in the batch for the maximum allowable time. This creates a transitive performance degradation. Because the vLLM scheduler calculates available memory in real-time to decide how many new requests can be added to a batch, a single zombie request significantly reduces the system's overall capacity.
The cost is not borne by the failing request alone, but by every healthy request in the queue. According to analysis by Kwon et al (2026), when even one degenerate request is running in parallel, the average latency for normal requests increases by anywhere from 15% to 71%. The healthy requests are not becoming more complex, nor is the computation increasing; they are simply starved of resources and stalled by a scheduler that is waiting for a loop to end.
For AI engineers, this shifts the conversation from model accuracy to infrastructure stability. Text degeneration is not a quality issue—it is a resource exhaustion issue. When a small percentage of requests can inflate total wall-clock time by over 40%, the standard practice of relying on average latency benchmarks becomes dangerous. The real metric for production readiness is not the mean, but the tail latency, specifically how the system handles the P99 outliers that threaten to collapse the entire pipeline's throughput.
Solving this requires moving beyond simple decoding tweaks and addressing the fundamental way models signal the end of a thought. Until the objective functions of LLMs evolve beyond the limitations of MLE, the industry will continue to fight a losing battle against the internal geometry of the loop.
Managing text degeneration is no longer just about improving the quality of the output, but about preventing a few broken requests from bankrupting the entire GPU cluster.
