Google's latest research into Supervised Reinforcement Learning (SRL) is making waves, promising to boost the reasoning abilities of smaller language models. The claim? That SRL can help these models tackle complex, multi-step reasoning tasks previously out of reach. But before we declare this a full-blown AI revolution, let's dig into the numbers and see if the story holds up. Google’s new AI training method helps small models tackle complex reasoning
The core problem SRL attempts to solve is the limitations of existing training methods. Reinforcement Learning with Verifiable Rewards (RLVR) rewards the model only for the final, correct answer. Supervised Fine-Tuning (SFT), on the other hand, relies on expert-created examples, which are both scarce and can lead to overfitting. SRL, according to the paper, strikes a balance by rewarding the model for each correct "action" in a sequence, rather than just the final outcome.
Google's researchers tested SRL on math reasoning and agentic software engineering tasks. In math, they fine-tuned Qwen2.5-7B-Instruct on a dataset of 1,000 difficult questions, achieving a 3.0% average performance boost compared to SFT and RLVR. In software engineering, they trained Qwen2.5-Coder-7B-Instruct on 5,000 expert trajectories, resulting in a 14.8% task resolve rate – a 74% relative improvement over SFT. These numbers look impressive at first glance.
But here's where the skepticism kicks in. A 3% improvement in math performance, while statistically significant (presumably), is hardly a game-changer. It's the kind of incremental gain we expect to see as models are tweaked and refined. The 74% relative improvement in software engineering sounds more dramatic, but let's not lose sight of the absolute numbers. The task resolve rate only increased to 14.8%. That means the model is still failing more than 85% of the time. And this is the part of the report that I find genuinely puzzling.
How were these "expert trajectories" generated? The paper mentions using a "powerful teacher model," but doesn't elaborate on its capabilities or potential biases. If the teacher model is already highly proficient at these tasks, then the student model is essentially just learning to mimic its behavior. That raises questions about the generalizability of the results. And if the "expert trajectories" are flawed, then the student model will learn those flaws as well. It's the classic "garbage in, garbage out" problem.
A Breakthrough, Or Just Better Fertilizer?
The Methodological Critique
The researchers claim that SRL encourages more flexible and sophisticated reasoning patterns, such as interleaved planning and self-verification. But how do we know this is happening? The paper provides no concrete evidence, such as detailed analysis of the model's internal representations or step-by-step breakdowns of its reasoning processes. We're left to take their word for it, which, frankly, isn't good enough.
Moreover, the paper notes that the strongest results came from combining SRL with RLVR. First, using SRL to teach foundational reasoning, then using RLVR to refine that skill. This "SRL-first" approach resulted in a 3.7% average increase. This suggests that SRL isn't a standalone solution, but rather a complementary technique that works best in conjunction with other methods. It's like saying a new type of fertilizer will boost crop yields, but only if you also use pesticides and irrigation.
The claim that SRL-trained models are more efficient in their reasoning also requires closer scrutiny. The paper states that token usage is roughly on par with the base model. But token usage is a crude metric. It doesn't tell us anything about the computational resources required for training or inference. It's possible that SRL-trained models are more efficient in terms of token usage, but less efficient in terms of overall energy consumption. (Energy consumption is a key factor in the economics of running these models.)
SRL is presented as a way to train smaller, less expensive models to achieve higher reasoning abilities. But how much smaller and less expensive are we talking about? The paper doesn't provide any concrete figures. It's possible that the cost savings are negligible, or that the smaller models sacrifice other important capabilities, such as language fluency or common-sense knowledge.
Ultimately, the value of SRL will depend on its ability to generalize to real-world problems. The experiments in the paper are limited to relatively narrow domains: math reasoning and software engineering. It remains to be seen whether SRL can be successfully applied to other areas, such as scientific discovery, medical diagnosis, or financial analysis.
Incremental Progress, Not a Paradigm Shift
Google's SRL research is undoubtedly interesting, and it may represent a step forward in the quest for more efficient and capable AI models. But let's not get carried away. The evidence presented in the paper suggests that SRL is an incremental improvement, not a revolutionary breakthrough. The numbers simply don't support the hype. It's clever math, yes, but a real AI breakthrough? The jury is still out.