The key point the paper seems to make is that existing benchmarks have relatively low complexity on reasoning complexity, so they made a new dataset DeepRD with arbitrarily large reasoning complexity and demonstrated that existing models fail at a complex enough problem. Complexity is defined from the complexity of a graph created by modeling the problem as a graph and determining the traversals needed to go from some source node to a target node.
My main critique is that I don't think there's evidence that this issue would persist after continuing to scale models to be larger and doing more RL. With a harness like what coding agents do these days and with sufficient tool use, I bet models could go much further on that reasoning benchmark. Otherwise, if the reasoning problem were entirely done within a single context window, it's expected that a complex enough reasoning problem would be too difficult for the model to solve.
My main critique is that I don't think there's evidence that this issue would persist after continuing to scale models to be larger and doing more RL. With a harness like what coding agents do these days and with sufficient tool use, I bet models could go much further on that reasoning benchmark. Otherwise, if the reasoning problem were entirely done within a single context window, it's expected that a complex enough reasoning problem would be too difficult for the model to solve.