Large language models have made strides in mathematical and logical reasoning, but they still hit a wall as problems become more complex. According to a new paper on arXiv, performance degrades as the number of constraints increases—a pattern the paper calls the 'curse of complexity.'
The paper, titled SAT-RL, proposes a training method that combines reinforcement learning with symbolic solvers. Rather than relying only on the model's own outputs, the training loop incorporates a SAT solver to provide exact logical feedback. The goal is to make the model's reasoning more robust and able to handle problems that go beyond the simple cases seen during training.
The abstract does not include experimental results, so the size of any improvement is not yet known. Even so, the approach is notable for explicitly coupling neural networks with classical symbolic tools, a hybrid direction that may help address known weaknesses in LLM reasoning.