Rift: Reinforcement Learning Achieves 2x Faster LLM Accelerator Fault Assessment with Scalable Methodology

The increasing complexity of artificial intelligence hardware demands new approaches to ensure reliability, as traditional fault assessment methods struggle with both computational cost and comprehensive failure detection. Khurram Khalil, Muhammad Mahad Khaliq, and Khaza Anuarul Hoque, all from the University of Missouri-Columbia, address this challenge with RIFT, a novel framework that leverages reinforcement learning to pinpoint the most critical failure scenarios in large language model accelerators. This innovative methodology dramatically accelerates fault assessment, achieving a 2. 2-fold speedup compared to existing techniques, and reduces the volume of necessary testing by over 99 percent, all while improving fault coverage. Importantly, RIFT not only identifies vulnerabilities but also guides the development of more efficient hardware protection strategies, demonstrating a 12. 8-fold improvement in cost-effectiveness compared to standard redundancy techniques, and seamlessly integrates into existing commercial verification workflows.

Reinforcement Learning Finds Critical Bit-Flips in LLMs Researchers developed RISE, a new framework for efficiently identifying critical bit-flips that can compromise the functionality of Large Language Models (LLMs) and other deep neural networks. This addresses the growing concern of hardware-induced errors and their potential to cause failures in AI systems, particularly those deployed in safety-critical applications. The core of RISE involves using Reinforcement Learning (RL) to intelligently explore the vast space of possible bit-flips and locate those that maximize the impact on the model’s output. The RISE framework employs an RL agent that learns a policy for selecting which bits to flip, receiving a reward based on the impact of those bit-flips on the model’s output, such as changes in prediction probability or incorrect answers. This actively searches the input space to discover the most vulnerable locations within the model. RISE is designed to be significantly more efficient than random or exhaustive bit-flip testing, and the reward function guides the RL agent to find bit-flips that cause the most significant changes in the model’s output. Experiments tested the framework on a variety of LLMs, including Llama 3, DeepSeek-v2, and GPT-3, evaluating performance on benchmarks like MMLU and other language understanding tasks.

Results demonstrate that RISE consistently outperforms baseline methods in terms of the number of bit-flips required to achieve a given level of performance degradation, identifying critical bit-flips that cause significant changes in the model’s output. This highlights the importance of hardware reliability and error mitigation techniques for AI systems.

This research confirms that Large Language Models are susceptible to bit-flip attacks, and that a Reinforcement Learning-based approach is effective in efficiently identifying critical bit-flips that cause the most significant impact on model performance. Software resilience techniques like RISE can complement hardware-level error mitigation techniques, and it is crucial to proactively assess and mitigate the risk of hardware-induced errors in AI systems, especially those deployed in safety-critical applications.

Reinforcement Learning Scales Accelerator Fault Assessment Researchers developed RIFT, a novel methodology for efficiently assessing faults in large AI accelerators, addressing the prohibitive computational costs of traditional techniques. The study pioneers a reinforcement learning approach that transforms the search for critical faults into a sequential decision-making problem, enabling scalable fault assessment for billion-parameter Large Language Models. Initially, the team employed hybrid sensitivity analysis to strategically prune the vast search space of potential faults, significantly reducing the computational burden before applying reinforcement learning. The core of RIFT involves training a reinforcement learning agent to intelligently generate minimal, high-impact test suites, effectively identifying the faults that cause the most significant errors. Experiments utilized NVIDIA A100 GPUs and billion-parameter LLM workloads to rigorously evaluate the framework’s performance.

Results demonstrate that RIFT achieves a 2. 2times fault assessment speedup compared to evolutionary methods, and reduces the required test vector volume by over 99% when compared to random fault injection, dramatically decreasing simulation time while maintaining comprehensive fault coverage. RIFT’s ability to inform intelligent hardware protection strategies was also demonstrated. RIFT-guided selective error correction code provides a 12. 8times improvement in cost-effectiveness, measured as coverage per unit area, compared to uniform triple modular redundancy. This represents a significant reduction in hardware overhead while maintaining robust error protection. To facilitate integration into existing design workflows, the team engineered RIFT to automatically generate UVM-compliant verification artifacts, ensuring seamless compatibility with commercial RTL verification tools.

Efficient Fault Assessment for Large AI Accelerators Researchers developed RIFT, a framework designed to efficiently identify critical failure points in massive AI accelerators, addressing limitations in traditional fault assessment methods. RIFT transforms the search for impactful faults into a sequential decision-making process, combining sensitivity analysis with reinforcement learning to generate minimal test suites. Evaluations using billion-parameter Large Language Models on A100 GPUs demonstrate RIFT achieves a 2. 2times speedup in fault assessment compared to evolutionary methods, and reduces the volume of test vectors required by over 99% while maintaining superior fault coverage. Experiments reveal that a complete collapse in functional accuracy, greater than 99% degradation, can be induced by perturbing an average of only 5. 4 ±0. 8 critical bits across evaluated models including GPT-2 Large, LLaMA 3. 1 8B, and DeepSeek-V2 7B. This substantiates the existence of sparse, high-impact failure modes and highlights the need for targeted fault assessment. Detailed analysis shows that 88. 5% of critical faults concentrate in attention mechanisms (47. 3%) and normalization layers (41. 2%), while feed-forward networks remain comparatively robust.

The team quantified the cost-effectiveness of different protection schemes, demonstrating that a RIFT-guided selective Error Correcting Code (ECC) strategy achieves 88. 5% fault coverage with just 13. 8% area overhead, yielding a cost-effectiveness of 6. 4. This represents a 12. 8times improvement compared to uniform Triple Modular Redundancy (TMR), which provides the highest coverage (99. 2%) but requires 205% area overhead and a cost-effectiveness score of only 0. 5. Statistical analysis of 15 independent trials confirms RIFT’s consistency, with the number of critical bits discovered tightly clustered around a mean of 5. 4 ±0. 8. The framework’s runtime scales linearly with the number of parameters in the target fault-sensitive hotspot, and the team observed a near-perfect linear fit (R2 0. 99). Memory requirements grow at a super-linear rate of approximately O(k1. 3). 👉 More information 🗞 RIFT: A Scalable Methodology for LLM Accelerator Fault Assessment using Reinforcement Learning 🧠 ArXiv: https://arxiv.org/abs/2512.09829 Tags: