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The TRM paper addresses this blog post. I don't think you need to read the HRM analysis very carefully, the TRM has the advantage of being disentangled compared

by tsoj 1y ago

The TRM paper addresses this blog post. I don't think you need to read the HRM analysis very carefully, the TRM has the advantage of being disentangled compared to the HRM, making ablations easier. I think the real value of the arcprize HRM blog post is to highlight the importance of ablation testing. I think ARC-AGI was supposed to be a challenge for any model. The assumption being that you'd need the reasoning abilities of large language models to solve it. It turns out that this assumption is somewhat wrong. Do you mean that HRM and TRM are specifically trained on a small dataset of ARC-AGI samples, while LLMs are not? Or which difference exactly do hint at?

shawntan 1y ago
> Do you mean that HRM and TRM are specifically trained on a small dataset of ARC-AGI samples, while LLMs are not? Or which difference exactly do hint at? Yes, precisely this. The question is really what is ARC-AGI evaluating for? 1. If the goal is to see if models can generalise to the ARC-AGI evals, then models being evaluated on it should not be trained on the tasks. Especially IF ARC-AGI evaluations are constructed to be OOD from the ARC-AGI training data. I don't know if they are. Further, there seems to be usage of the few-shot examples in the evals to construct more training data in the HRM case. TRM may do this via the training data via other means. 2. If the goal is that even _having seen_ the training examples, and creating more training examples (after having peeked at the test set), these evaluations should still be difficult, then the ablations show that you can get pretty far without universal/recurrent Transformers. If 1, then I think the ARC-prize organisers should have better rules laid out for the challenge. From the blog post, I do wonder how far people will push the boundary (how much can I look at the test data to 'augment' my training data?) before the organisers say "This is explicitly not allowed for this challenge." If 2, the organisers of the challenge should have evaluated how much of a challenge it would actually have been allowing extreme 'data augmentation', and maybe realised it wasn't that much of a challenge to begin with. I tend to agree that, given the outcome of both the HRM and this paper, is that the ARC-AGI folks do seem to allow this setting, _and_ that the task isn't as "AGI complete" as it sets out to be.
- shawntan 1y ago
  I should probably also add: It's long been known that Universal / Recursive Transformers are able to solve _simple_ synthetic tasks that vanilla transformers cannot. Just check out the original UT paper, or some of it's follow ups: Neural Data Router, https://arxiv.org/abs/2110.07732 https://arxiv.org/abs/2110.07732; Sparse Universal Transformers (SUT), https://arxiv.org/abs/2310.07096 https://arxiv.org/abs/2310.07096. There is even theoretical justification for why: https://arxiv.org/abs/2503.03961 https://arxiv.org/abs/2503.03961 The challenge is actually scaling them up to be useful as LLMs as well (I describe why it's a challenge in the SUT paper). It's hard to say with the way ARC-AGI is allowed to be evaluated if this is actually what is at play. My gut tells me, given the type of data that's been allowed in the training set, that some leakage of the evaluation has happened in both HRM and TRM. But because as a field we've given up on actually carefully ensuring training and test don't contaminate, we just decide it's fine and the effect is minimal. Especially considering LLMs, the test set example leaking into the dataset is merely a drop in the bucket (I don't believe we should be dismissing it this way, but that's a whole 'nother conversation). With these models that are challenge-targeted, it becomes a much larger proportion of what influences the model behaviour, especially if the open evaluation sets are there for everyone to look at and simply generate more. Now we don't know if we're generalising or memorising.
  - jononor 11mo ago
    I think that the best way to address this potential ARC overfitting, would be to create more benchmarks - that are similar in concept, focusing on fluid intelligence, but from another angle than ARC. Of course it is quite costly and also requires some "marketing" to actually get it established.
    - shawntan 11mo ago
      This would not help if no proper constraints are established on what data can and cannot be trained on. And maybe just figuring out what the goal of the benchmark is. If it is to test generalisation capability, then what data the model being evaluated is trained on is crucial to making any conclusions. Look at the construction of this synthetic dataset for example: https://arxiv.org/pdf/1711.00350 https://arxiv.org/pdf/1711.00350

by tsoj 1y ago

shawntan 1y ago

> Do you mean that HRM and TRM are specifically trained on a small dataset of ARC-AGI samples, while LLMs are not? Or which difference exactly do hint at? Yes, precisely this. The question is really what is ARC-AGI evaluating for? 1. If the goal is to see if models can generalise to the ARC-AGI evals, then models being evaluated on it should not be trained on the tasks. Especially IF ARC-AGI evaluations are constructed to be OOD from the ARC-AGI training data. I don't know if they are. Further, there seems to be usage of the few-shot examples in the evals to construct more training data in the HRM case. TRM may do this via the training data via other means. 2. If the goal is that even _having seen_ the training examples, and creating more training examples (after having peeked at the test set), these evaluations should still be difficult, then the ablations show that you can get pretty far without universal/recurrent Transformers. If 1, then I think the ARC-prize organisers should have better rules laid out for the challenge. From the blog post, I do wonder how far people will push the boundary (how much can I look at the test data to 'augment' my training data?) before the organisers say "This is explicitly not allowed for this challenge." If 2, the organisers of the challenge should have evaluated how much of a challenge it would actually have been allowing extreme 'data augmentation', and maybe realised it wasn't that much of a challenge to begin with. I tend to agree that, given the outcome of both the HRM and this paper, is that the ARC-AGI folks do seem to allow this setting, _and_ that the task isn't as "AGI complete" as it sets out to be.

shawntan 1y ago
I should probably also add: It's long been known that Universal / Recursive Transformers are able to solve _simple_ synthetic tasks that vanilla transformers cannot. Just check out the original UT paper, or some of it's follow ups: Neural Data Router, https://arxiv.org/abs/2110.07732 https://arxiv.org/abs/2110.07732; Sparse Universal Transformers (SUT), https://arxiv.org/abs/2310.07096 https://arxiv.org/abs/2310.07096. There is even theoretical justification for why: https://arxiv.org/abs/2503.03961 https://arxiv.org/abs/2503.03961 The challenge is actually scaling them up to be useful as LLMs as well (I describe why it's a challenge in the SUT paper). It's hard to say with the way ARC-AGI is allowed to be evaluated if this is actually what is at play. My gut tells me, given the type of data that's been allowed in the training set, that some leakage of the evaluation has happened in both HRM and TRM. But because as a field we've given up on actually carefully ensuring training and test don't contaminate, we just decide it's fine and the effect is minimal. Especially considering LLMs, the test set example leaking into the dataset is merely a drop in the bucket (I don't believe we should be dismissing it this way, but that's a whole 'nother conversation). With these models that are challenge-targeted, it becomes a much larger proportion of what influences the model behaviour, especially if the open evaluation sets are there for everyone to look at and simply generate more. Now we don't know if we're generalising or memorising.
- jononor 11mo ago
  I think that the best way to address this potential ARC overfitting, would be to create more benchmarks - that are similar in concept, focusing on fluid intelligence, but from another angle than ARC. Of course it is quite costly and also requires some "marketing" to actually get it established.
  - shawntan 11mo ago
    This would not help if no proper constraints are established on what data can and cannot be trained on. And maybe just figuring out what the goal of the benchmark is. If it is to test generalisation capability, then what data the model being evaluated is trained on is crucial to making any conclusions. Look at the construction of this synthetic dataset for example: https://arxiv.org/pdf/1711.00350 https://arxiv.org/pdf/1711.00350