The question posed by Relative_Wallaby_823 after watching the Jev presentation is the right one, and it deserves a straight answer. If the model's output is limited to Choice, Score, or Noul, then the entire pipeline is indeed differentiable. Cross-entropy or mean squared error would give you a clean gradient. So why add reinforcement learning? The suspicion that this is marketing fluff is understandable, but it likely misses the deeper structural issue. The real problem is not the loss function; it is the sequential nature of the task. When a model decides to look at a cell, then decides to filter a column, then decides to sum a range, it is performing a sequence of discrete actions. Each action changes the state of the spreadsheet, and the final score is only known at the end. That is not a classification problem. That is a trajectory optimization problem, and that is where RL becomes relevant.
You can train a model to predict a single score for a static input using supervised learning, but a spreadsheet agent is not making a single prediction. It is making a series of decisions where the outcome of one action affects the next. This is precisely the domain where behavioral cloning fails. If you train on human demonstrations, the model learns to mimic what people do, but it never learns to correct its own mistakes. It cannot recover from a bad filter or a miscalculated range because it never experiences the consequence of those actions. This is the same challenge we see in Unlock LLM Training: A Practical Guide to Distributed Algorithms where the focus is on scaling training across systems, but the core insight is similar: the training objective shapes the behavior. Supervised learning optimizes for matching the teacher. Reinforcement learning optimizes for the final outcome, which is a fundamentally different target.
For our readers, the practical takeaway is this: the absence of a visible RL environment does not mean the approach is fake. It means the environment is the spreadsheet itself, with its grid, its formulas, and its infinite possible states. This is not a toy grid-world. It is a high-dimensional, partially observable environment where the model must learn to explore. The Exploring Paragraph Structure: How LLMs Navigate Token Space article touches on how models build internal representations of structure. For Jev, the structure is the spreadsheet layout, and the RL signal is the final correctness of the answer. The question is not whether RL is needed. It is whether the team can make the reward signal dense enough to avoid the long-horizon sparsity problem. That is the real engineering challenge.
So, what would we tell a reader who asks if RL is just for marketing? We would say this: if Jev only ever predicted a single value from a static snapshot, then yes, RL would be unnecessary. But the moment the model takes a step, the problem changes. The fact that the outputs are differentiable is necessary, but not sufficient. The missing piece is credit assignment across steps. That is what RL provides. The open question to watch is whether they are using a policy gradient method, a Q-learning variant, or a hybrid. That detail will tell you more about the actual difficulty of the task than any presentation slide. For now, the healthy skepticism is warranted, but the conclusion should be cautious curiosity, not dismissal.