Transformers Provably Solve Parity Efficiently with Chain of Thought
transformers;chain of thought;parity;self-consistency
Yes
No ethics review needed.
I did not came up with any useful and educated questions. I believe the paper is of high quality and will need to study it in more depth.
I find the work to be of very high quality and very relevant to the current research in reasoning abilities of language model. The theoretical setup is in my opinion well chosen, very concise and easy to understand.  Even though this is a theoretical paper, the authors are well aware of the current research on the applied side and the questions studied reflect it. I believe it is a strong accept, but I'm giving accept because I'm not sure whether theoretical results of this kind should have spotlight talks at ICLR.
The paper provides a theoretical foundations which explain the benefits of chain-of-though. The authors study a simple setup of k-parity problem with 1-layer Transformer and in this case provide a separation results for transformer trained without intermediate supervision and one trained with teacher forcing. They also provide positive results for the case where the transformer can generate chain-of-thought but is not explicitly supervised with the intermediate results. They show conditions which are required in this case (data augmentation and self-consistency checking).
I was not able to judge how limiting is the setup described in the paper overall, but for example the special masking seems quite artificial but I understand that it is required for the theoretical results. It would be nice if the authors explicitly describe the limitations they are aware of.
Yes
No ethics review needed.
1. For the k-parity problem, each layer’s intermediate state corresponds to a simple “2-parity” check, which is straightforward to define. In more complex tasks, however, defining such intermediate states may not be as intuitive. How could this approach be extended to real-world reasoning tasks where intermediate state decomposition is less clear?
2. The paper uses data augmentation to validate intermediate states in the reasoning process. If applied to practical reasoning tasks, would this data augmentation method require significant adjustments to be effective?
3. Given the proposed architecture, are there potential challenges that might arise in real-world training, particularly concerning stability and computational complexity?
1. The paper innovatively uses the k-parity problem to theoretically analyze how transformers develop stepwise reasoning capabilities.
2. This paper introduces a novel hierarchical decomposition of the k-parity problem and designs a corresponding transformer architecture to handle this structure effectively.
3. This paper proposes mechanisms like data augmentation and self-consistency checks, enabling transformers to perform CoT reasoning even in the absence of explicit intermediate supervision.
4. The paper provides clear and logically rigorous analysis for the k-parity problem, offering strong, well-structured insights within this specific context.
This paper provides a theoretical exploration of how transformers develop stepwise reasoning capabilities through recursive task decomposition, using the k-parity problem as a case study. Through formal mathematical proofs and asymptotic complexity analysis, the authors establish three key findings: without intermediate supervision, solving k-parity with finite-precision gradient-based algorithms requires exponentially many steps, extending prior impossibility results to finite-sample settings. In contrast, with teacher forcing—where ground-truth intermediate steps are provided—the model efficiently learns the task in a single gradient update, underscoring the value of explicit supervision for complex reasoning. Additionally, even without teacher forcing, the model achieves accurate reasoning by employing data augmentation to validate intermediate steps, implementing a form of self-consistency check that allows for logarithmic convergence. This research emphasizes theoretical analysis through mathematical proofs and complexity analysis, offering insights into how transformers can perform chain-of-thought reasoning and task decomposition, thereby aligning with recent empirical advances in complex reasoning tasks.
1. The theoretical analysis is heavily focused on the k-parity problem, which, while illustrative, may not extend seamlessly to more complex or varied reasoning tasks. This limits the applicability of the findings to broader transformer applications.
2. The paper lacks empirical experiments to support the theoretical conclusions, which could leave readers questioning the practical effectiveness of the proposed methods in real-world scenarios or on diverse datasets.
Yes
No ethics review needed.
Q1. Is there any particular reason why one-layer transformer has been chosen? Would the same results (or even stronger) apply to 2 layer transformer?

Q2. Could we apply the same idea to less structured task which lack the hierarchical structure, hence the "easier" teaching forcing?
S1. The study is very rigorous, sharing some lights on the importance of CoT on multi hop reasoning. 

S2. The introduction of the causal mask is indeed very simple to explain and very intuitive, but also very effective to limit the error compounding. I think this is the main contribution, given teaching forcing is not really useful on this problem.

S3. This work extended previous related work on more realistic setting, offering a clearer picture of why CoT is indeed essential for the given problem.
Authors showed that CoT here is beneficial to learn a very hard problem, i.e. bit subset parity. Authors trained a one-layer transformer with a fixed CoT and with a tree based approach to determine the subset and they showed that without CoT it's not possible to learn an algorithm to detect the right bit, _assuming_ data cannot be manipulated freely. They introduced a causal mask to allow less error propagation during CoT.
W1. Main weakness is a lack of empirical validation. I would love to see just a trained model on various settings (like with and without teacher forcing, parity problem sizes with different $k$ and $d$ values) to show that without teaching forcing the model is indeed able to learn the task. 

W2. The claim on the conclusion about finetuning transformer to improve multi step reasoning seems really too strong. In particular, it would be nice to show that it's easy to produce CoT data for all reasoning tasks before claiming this. So I would rephrase that sentence or provide a more detailed discussion on why authors believe their results may extend to other reasoning tasks.
@inproceedings{
anonymous2024transformers,
title={Transformers Provably Solve Parity Efficiently with Chain of Thought},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=n2NidsYDop},
note={under review}
}
This work provides the first theoretical analysis of training transformers to solve complex problems by recursively generating intermediate states, analogous to fine-tuning for chain-of-thought (CoT) reasoning. We consider training a one-layer transformer to solve the fundamental $k$-parity problem, extending the work on RNNs by \citet{Wies23}. We establish three key results: (1) any finite-precision gradient-based algorithm, without intermediate supervision, requires substantial iterations to solve parity with finite samples. (2) In contrast, when intermediate parities are incorporated into the loss function, our model can learn parity in one gradient update when aided by \emph{teacher forcing}, where ground-truth labels of the reasoning chain are provided at each generation step. (3) Even without teacher forcing, where the model must generate CoT chains end-to-end, parity can be learned efficiently if augmented data is employed to internally verify the soundness of intermediate steps. These results rigorously show that task decomposition and stepwise reasoning naturally arise from optimizing transformers with CoT; moreover, self-consistency checking can improve reasoning ability, aligning with empirical studies of CoT.
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transformers chain of thought parity self-consistency
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Transformers Provably Solve Parity Efficiently with Chain of Thought
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