Rethinking Reinforcement Learning: Beyond Depth to Complexity

Reinforcement learning with verifiable rewards (RLVR) is evolving. Traditionally, studies focused narrowly on reasoning depth. But that's only part of the story. Strip away the marketing and you get a broader picture of the reasoning space, one that includes both difficulty and reward in new dimensions.

Beyond Depth: Complexity Matters

Let's break this down. Difficulty in reasoning isn't just about depth. It's also about navigating complex environments. Models need to pick the right path amidst distractors and interacting structures. This isn't just theoretical. It's a practical challenge models must overcome to succeed in real-world applications.

Rewarded reasoning form also needs rethinking. Four core abilities are spotlighted: deductive state tracking, abductive recovery of hidden events, inductive rule induction, and analogical transfer. These aren't just fancy terms. They represent essential skills for any reasoning model that wants to be truly effective.

The Synthetic Knowledge-Graph Experiment

To explore these new dimensions, researchers constructed a synthetic knowledge-graph environment. Each instance varied along depth, complexity, and task family. Here's what the benchmarks actually show: models tackling both depth and complexity outperform those focusing on just one axis.

Interestingly, reasoning families don't respond uniformly. Abductive reasoning struggles outside its RL-covered comfort zone, while task correlations cluster into deductive-abductive and inductive-analogy pairs. This isn't just academic, it's a wake-up call for model developers to consider broader training regimes.

Uniform Mixing vs. Staged Curricula

Another key finding: uniform mixing of tasks outperforms staged curricula under a fixed training budget. This suggests that diversity in training tasks isn't just beneficial, it's key. Why settle for less when a mixed approach can yield better results?

recent off-the-shelf models show the same deductive-over-abductive asymmetry. This isn't just a quirk of controlled environments. It's a fundamental challenge that needs addressing if models are to become more versatile.

So, why does this matter? In a world where AI's role is expanding, understanding complex reasoning dynamics is more critical than ever. As models evolve, so too must our approach to training them. The architecture matters more than the parameter count.