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Why RL Reinforces Verbose Comment Blocks
I’ve heard a lot of talk about LLMs recently, and among the most common topics of discussion have been the comments. LLM comments generally seem to suck: they are verbose, regardless of surrounding context, they seem to encode conversation decisions, and they tend to use made-up vocabulary or terms. Engineers have been working on ways to reduce the pain, by carefully crafting their prompts or even separately running “desloppifier” agents to clean up PRs. In my experience, these techniques are only mildly successful.
So why do LLMs keep writing comments like these, even as their software benchmark scores climb ever higher with new model releases? I’d like to argue that they remain bad precisely because the scores in benchmarks are getting better. And very likely they will degrade, in the same way that model tool calling has gotten worse over time.
These comments are not for you, my (hopefully human) reader.
How Agents Read and Rely on Comments
Fundamentally, as many in the coding agent space have pointed out, most modern models have been RL’ed to fit into agentic harnesses. These workflows are part of their training. And the way that RL works is that it is outcome-based. A model takes steps, edits files, does whatever it does in its agentic framework, and either arrives at a solution or doesn’t. Behaviors that contributed to successful outcomes are encouraged, and become more common.
The difficulty is that as benchmarks get harder and as models are asked to take on larger and larger chunks of the software development workflow, their ability to keep information “just” within their context is pushed closer to its limit. Session compaction can accidentally destroy design decisions or rationale, forcing the model to eventually re-discover previous decisions or even switch directions.
Comments are a hugely useful persistent store of contextual information. If a model edits a file, chances are it will read it as well, discover the comments, and re-load the given information into context. If one agent makes a change to a file with some design rationale — “array, not a linked list” — another agent that might want to change it back will spot that and defer to the pre-existing rationale. It should come as no surprise that agents that make use of verbose comments succeed more frequently, and get rewarded. The result: +50/-1 comment blocks in your diff. It is irrelevant whether these comments concisely describe the codebase; their intended audience can read and parse them instantly. The comments are not for you.
The Evaluation Incentive Behind Comment Style
It’s very hard to prompt this behavior out, and there’s a pretty good chance that you don’t want to, by the simple evolutionary argument: these things have helped the model do well in evaluations. By removing them, you are likely undermining part of whatever mechanism makes it tick. On top of that, you are fighting the wiring it has developed to do exactly this. It’s like trying to get humans to stop liking hyperpalatable foods.
So then, the natural conclusion is that we should be leaving these comments as they are, right? As long as they’ve been known to improve agents’ performance, the more the better? Some, who have leaned heavily on models for self-regulating via persistent state, have found fascinating emergent behaviors, including whole organizational structures. They believe that to be the future. However, in my opinion, it may not be that simple. As we’ve seen with human evolutionary adaptations, they don’t always do well outside of the environment in which they arose. For instance — humans love hyperpalatable foods. They will eat them in excess, which can lead to obesity and a variety of other conditions. These too are emergent behaviors, and likely quite interesting from a medical perspective. That does not make them desirable.
In the same way that scarcity of sugars and fats in nature balanced (and motivated) human enjoyment of them, it’s possible that the ephemeral nature of software engineering “tasks” is counteracting the accumulation of LLM-generated commentary. In real-world contexts, human attention and editing may be doing the same thing. We are yet to see what codebases maintained entirely with RL’ed agentic behaviors over years look like, and whether there are limitations.
Regardless of whether this new style holds up in the extreme, it’s that way for a reason — and you are no longer its sole intended audience.