The Rise and Fall of Reinforcement Learning for LLM “Reasoning”

In the fast-moving world of artificial intelligence, we've been dazzled by increasingly impressive claims about "reasoning models" and their problem-solving abilities. These models, enhanced through Reinforcement Learning for Reasoning (RLfR), supposedly demonstrate cognitive capabilities far beyond their predecessors. But a sobering collection of recent papers suggests we might be witnessing sophisticated optimization rather than genuine reasoning breakthroughs.

Beneath the Benchmark Scores

The AI industry has a penchant for celebrating benchmark improvements, but these numbers may obscure a fundamental reality. According to Yue et al.'s research (arXiv:2504.13837), "Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?", what we're witnessing isn't the emergence of new reasoning capabilities but rather the optimization of pre-existing patterns.

Their findings reveal that while RLfR-trained models achieve impressive pass@1 rates (getting the right answer on the first try), the underlying base models could often find those same correct answers given enough attempts (high pass@K values). This suggests something profound: the reasoning abilities displayed by these "enhanced" models were largely present in the base models all along.

The Mechanics Behind the Performance Boost

What's actually happening when we observe these seemingly remarkable reasoning improvements? Several papers paint a consistent picture. Reinforcement Learning via Chain of Thought (RL via CoT) relies heavily on prompt techniques to drive generation—what could be described as "stochastic funneling." These techniques might leverage a "token cushion" to bring certain activations into focus, but they remain fundamentally reliant on the latent space established during pretraining.

As one paper puts it, "performance is bounded by pretraining, not RL." The pretraining phase sets the search space that RL merely optimizes within. If fine-tuning has already been applied, the latent space may already be distorted or collapsed, removing the combinatorial expanse that gives language models their power. In such cases, performance can only decrease from the original pretraining potential.

Methodology Problems Compound the Confusion

"A Sober Look at Progress in Language Model Reasoning" (arXiv:2504.07086) highlights another crucial issue: our evaluation methods often lack rigor. The paper argues that progress in language model reasoning "often outpaces methodological rigor," with evaluations lacking transparency, robustness, or statistical grounding.

Performance gains can hinge on subtle, unreported implementation choices like decoding parameters, random seeds, prompt formatting, and even hardware/software differences. What appears as a breakthrough might simply be the result of favorable experimental conditions that won't generalize.

Pattern Matching vs. True Reasoning

"(How) Do reasoning models reason?" (arXiv:2504.09762v1) suggests that improvements in Large Reasoning Models (LRMs) are more attributable to better pattern matching and plausible output generation for specific learned tasks, rather than a fundamental enhancement of underlying reasoning.

These models still struggle with generalization to variations or novel scenarios. They can generate incorrect outputs for unsolvable problems, sometimes with confident but false justifications. This aligns with findings from "GSM-Symbolic," which demonstrated that LLM performance on mathematical reasoning declines dramatically when only numerical values are altered from templated questions.

The Fragility of Apparent Understanding

Perhaps most damning is the evidence of fragility. Adding a single, seemingly relevant but logically unnecessary clause to a mathematical problem can cause performance drops of up to 65%. This extreme sensitivity to minor input perturbations strongly suggests these systems aren't engaging in genuine logical reasoning but instead replicating reasoning steps from their training data.

Another paper evaluating the world models implicit in generative models found them to be "far less coherent than they appear." This incoherence leads to fragility where models fail on related but subtly different tasks, even if they perform well on existing diagnostics.

What We're Really Seeing

When we observe a "reasoning model" solving complex problems, we're not witnessing cognition comparable to human thought. We're seeing a system that has become exceptionally good at sampling from statistical distributions shaped by reinforcement learning to produce outputs that match our expectations of what reasoning should look like.

The model becomes better at responding efficiently to reward signals and sampling patterns that have been previously successful. But this doesn't mean it understands concepts, makes genuine logical inferences, or possesses anything approaching human reasoning.

One paper goes so far as to state that despite occasional "flashes of brilliance," GPT-4 is "utterly incapable of reasoning" in a genuine sense. While this might be an extreme position, it emphasizes the growing skepticism about claims of AI reasoning.

A More Grounded Perspective

None of this diminishes the remarkable engineering achievement these systems represent. They are extraordinary tools that can generate valuable responses across countless domains. But attributing true intelligence or novel reasoning capacities to them—without acknowledging their fundamental pattern-matching architecture—risks confusing sophisticated optimization with genuine cognition.

As we move forward with AI development, this distinction matters. Understanding the limitations of current approaches might help us develop systems that can truly reason, rather than just becoming increasingly efficient at appearing to do so. RL for reasoning, despite its initial promise, may ultimately be remembered as a technique that refined outputs rather than revolutionized AI thinking.​​​​​​​​​​​​​​​​