Hierarchical Tokenization Is Having a Moment in Robot Learning, But Let's Be Precise About What That Means

A parallel line of work, HiMAQ (hierarchical macro action quantization), takes a different angle on the same structural insight. This paper, also recently posted to arXiv, focuses on making reinforcement learning agents more human-like. The motivation here is interpretability and reliability rather than raw performance. The argument is that most RL agents are reward-driven in ways that produce behaviors humans find confusing or unpredictable, which limits their usefulness in collaborative settings.

The HiMAQ approach encodes human demonstrations into macro actions using the same two-level vector quantization structure. Lower level maps to fine-grained subaction clusters, higher level aggregates into action clusters. The evaluations on D4RL benchmarks show improvements in human-likeness scores while maintaining comparable or better success rates. I know I'm being picky here, but "human-likeness scores" is doing a lot of work in that sentence. The paper uses specific metrics for this, but whether those metrics actually capture what makes behavior feel human-like to human observers remains an open question. Quantifying human-likeness is notoriously difficult, and different metrics often disagree with each other.

What I find genuinely new in the HiMAQ work is the demonstration that the hierarchical approach generalizes across multiple RL algorithms (IQL, SAC, and RLPD). This suggests the benefit isn't specific to a particular learning paradigm but rather reflects something more fundamental about how action spaces should be structured. That's a stronger claim than showing improvement on one algorithm, though the sample size of three algorithms is still small.

The third piece of recent work takes a somewhat different approach to the memory problem in robot learning. Notes-to-Self, published on arXiv, addresses the fact that many manipulation tasks are non-markovian. Actually, the research shows that existing vision-language-action models are primarily "stateless" in a way that makes them struggle with tasks requiring memory of what happened earlier in an episode. The solution here is a language scratchpad that allows the model to write notes to itself about object positions, plans, and progress toward subgoals.

This is a different kind of hierarchy than the action tokenization approaches, but it shares the insight that flat representations are insufficient for complex manipulation. The scratchpad essentially creates a temporal hierarchy through language, allowing the model to maintain state across long horizons. The evaluations on ClevrSkills, MemoryBench, and a real-world pick-and-place task show significant improvements for both non-recurrent and recurrent models.

It's worth noting that the ClevrSkills environment, while useful for controlled evaluation, involves relatively simple objects and scenes compared to real-world clutter. The real-world pick-and-place task is a stronger validation, but the paper doesn't provide extensive details on the diversity of objects or the failure modes. This isn't a criticism unique to this paper; it's endemic to the field. We evaluate on the environments we have, and those environments are often cleaner than the real world.

So what connects these three papers? The underlying intuition is that robot actions have structure at multiple scales, and learning algorithms that respect that structure outperform those that don't. A reaching motion isn't just a sequence of joint angles; it's a high-level intention (reach toward the cup) instantiated through mid-level motor primitives (extend arm, orient wrist) executed as low-level control signals. Hierarchical tokenization attempts to recover something like this structure from data, without requiring explicit engineering of the hierarchy.

This is, in a way, a return to ideas from classical robotics and motor control. The notion of motor primitives and hierarchical control has been around for decades. What's different now is the claim that these hierarchies can be learned from data rather than hand-designed, and that they can be represented as discrete tokens compatible with modern sequence models. The connection to large language models is not coincidental. If you can tokenize actions the way we tokenize words, you can potentially leverage the same architectures and training approaches that have proven so effective for language.

But I want to pump the brakes a bit on the enthusiasm here. The evaluations in these papers, while rigorous within their scope, are limited in ways that matter. The simulation benchmarks (D4RL, ClevrSkills, various manipulation suites) are useful but not representative of the full complexity of real-world robotics. The real-world evaluations are typically on single tasks or small task sets, with limited variation in objects, lighting, and other conditions. We don't know yet how well these approaches transfer across robots, across task domains, or across the kinds of distribution shifts that occur in deployment.

There's also a question about what the right level of hierarchy is. The papers I've discussed use two levels, but why two? Is this optimal, or just convenient? Some manipulation tasks might benefit from three or four levels; others might be better served by a single level with a larger codebook. The choice of hierarchy depth is often presented as an architectural decision rather than something learned from data, which feels like a missed opportunity.

The temporal aspects remain underexplored. HiST-AT incorporates timestamps, which is good, but the relationship between temporal structure and spatial structure in actions is complex. Human motor control involves intricate timing dependencies that current tokenization schemes may not capture. When you pour water from a pitcher, the timing of the tilt, the duration of the pour, and the speed of the correction are all coupled in ways that a simple timestamp recovery objective might not learn.

I also want to flag a concern about evaluation metrics. "State-of-the-art" claims in robot learning are notoriously difficult to compare across papers because of differences in evaluation protocols, task definitions, and success criteria. The field would benefit from more standardized benchmarks with held-out test sets that aren't available to researchers during development. Right now, it's too easy to tune methods to specific benchmarks in ways that don't generalize.

What I'd want to see next is a systematic comparison of these hierarchical approaches against each other and against simpler baselines on a common benchmark suite. The papers I've discussed use different evaluation setups, making it hard to know whether HiST-AT, HiMAQ, or Notes-to-Self would perform better on any given task. A unified evaluation would also help identify whether the benefits of hierarchy are additive (combining HiST-AT's temporal tokenization with Notes-to-Self's scratchpad, for example) or whether they're solving the same underlying problem in different ways.

There's also the question of computational cost. Hierarchical approaches typically require more parameters and more computation than flat approaches. The papers don't always report inference times or memory requirements in ways that allow direct comparison. For robotics applications where real-time control matters, this could be significant.

Despite these caveats, I think the convergence on hierarchical representations is meaningful. When multiple research groups independently arrive at similar architectural choices, it often indicates that there's something real being captured. The specific implementations differ (two-level vector quantization, language scratchpads, temporal versus spatial emphasis), but the underlying insight is shared: robot actions have structure that flat representations fail to exploit.

The connection to in-context learning is particularly intriguing. If hierarchical tokenization enables robots to learn new tasks from a handful of demonstrations, that addresses one of the key bottlenecks in robot deployment. Collecting large datasets for every task is impractical; being able to teach a robot a new task by showing it a few examples is much closer to how we'd actually want to use these systems.

But we're not there yet. The "few examples" in current in-context learning work is typically 10 to 50 demonstrations, which is better than thousands but still requires careful data collection. The tasks are often relatively simple by human standards. And the generalization to truly novel objects and environments remains limited.

I'll end with a methodological note. All three papers I've discussed are available on arXiv, which means they haven't gone through peer review (or in the case of the replace announcements, have been revised since initial posting). This is increasingly common in machine learning and robotics, and it's not necessarily a problem. But it does mean we should treat the claims with appropriate caution. Peer review isn't perfect, but it does catch some errors and overstatements. The rapid pace of publication in this field sometimes means that ideas get ahead of the evidence supporting them.

Hierarchical action tokenization is a promising direction. The recent papers advance our understanding of how to structure action representations for robot learning. But the field has a tendency toward hype cycles, and I'd rather see steady progress than breathless announcements of breakthroughs. The work is good. Let's just be precise about what it shows and what remains to be demonstrated.

James Chen · 6 hours ago · 4 min