For 2$ This Method Solve ARC-AGI v1 at 27.5%?

Name: For 2$ This Method Solve ARC-AGI v1 at 27.5%?
Uploaded: 2026-02-05T14:15:47.000Z
Duration: 2 h 24 min 26 s

A New Power Tool for Arc AGI v1

Overview of the Solution

A new power tool for arc AGI v1 has been developed by Matilv, an AI researcher from IAT Bombay, achieving 27.5% on RKGI v1 with only $2 worth of compute.

There is skepticism online regarding the legitimacy of this solution due to its training on some test information.

Development and Results

Matilv published two blog posts discussing previous failures in RKGI solvers, attributing it to improper compression of useful information.

After starting his project on December 5th, he initially achieved a 12% success rate and later reached 27% within two weeks for just $2.

Discussions indicate that his approach is reproducible and valid, presenting a novel perspective on the problem.

Related Work in RKGI

The speaker categorizes related work into non-scientific universal puzzle solver methods, emphasizing their limitations as grid solvers rather than true puzzles.

Three main approaches are highlighted: Compress Arc (similar lineage), HRM (controversial), and TRM (easier to understand).

Compress Arc Methodology

Key Concepts

The Compress Arc method involves training a neural network to reconstruct compressed information from inputs to generate outputs effectively.

It utilizes input data compression techniques to enhance output accuracy through decompression processes.

Learning Process

The learning process shows progressive understanding over iterations; after 50 steps, the model begins recognizing patterns and colors more accurately at later stages.

Despite achieving good results (34% on training set, 20% on evaluation set), the architecture requires extensive training time—130 hours for significant progress.

HRM vs. TRM Approaches

HRM Complexity

The HRM approach features complex hierarchical layers working together but requires specific puzzle embeddings for effective operation.

TRM Simplification

The TRM simplifies many aspects found in HRM while still addressing similar problems without excessive complexity.

This structured overview captures key insights from the transcript while providing timestamps for easy reference.

Understanding TRM and HRM Methodologies in Data Augmentation

Overview of HRM and Data Augmentation

The HRM (Hierarchical Reinforcement Model) requires a significant data augmentation process to perform effectively at the RGI (Reinforcement Goal Indicator).

A recommended range for augmentation is between 300 and 1,000 instances, with 300 being sufficient for initial testing.

Outputs from the augmented data are selected through majority voting to determine the final output as the "passet 2."

Transitioning to TRM

The TRM (Temporal Reinforcement Model) is described as cleaner than HRM, simplifying many processes while retaining core concepts.

It involves updating latent variables (Z), akin to an encoder, iterating this process multiple times—up to 300 iterations may yield improvements.

The best model identified during ablation studies utilizes two layers.

Compression Techniques in TRM

Both HRM and TRM do not heavily rely on traditional compression methods; however, latent variable Z can be viewed as a form of compression.

An important insight is that certain information isn't optimally utilized from a complexity standpoint; compressing this information could enhance RGI performance.

Importance of Example Inputs and Private Puzzles

Example inputs are crucial for inference and should be compressed for better efficiency during testing phases.

Private puzzles contain valuable information that can be compressed since they share foundational concepts with training puzzles.

Test Time Training Concept

Test time training allows models to learn from both outputs and inputs simultaneously, enhancing performance benchmarks significantly.

The method referred to as RKGI operates without pre-training by focusing solely on test time training during evaluation phases.

Controversial Aspects of Input Compression

Compressing test inputs remains a contentious topic within the community; some practitioners oppose it despite its potential benefits.

Most current methodologies utilize supervised learning with fixed inputs, contrasting with unsupervised approaches that consider both input and output grids simultaneously.

Learning Objectives in Testing Phase

During testing, models aim to predict both input grids and output grids concurrently, necessitating comprehensive learning strategies.

Test Time Training: Insights and Controversies

Overview of Test Time Training

The discussion begins with the concept of training on both the training set and evaluation (evol) set simultaneously during test time, raising questions about its validity.

Concerns are expressed regarding the approach of using full-blown training mode at test time, suggesting it may be excessive to compress all information at once.

Technical Aspects of the Model

The architecture discussed is a straightforward four-layer transformer without complex features like multi-level recursion or variational autoencoders.

Notable elements include 28 parameters and a unique 3D rope for position embedding, which differs from traditional 2D approaches. This design aims to incorporate grid-like structures into the model's reasoning.

Embedding Techniques and Augmentation

The use of standard autoregressive training is highlighted as an interesting aspect, likening it to large language models (LLMs).

A per-task embedding method is mentioned, contrasting with previous models that utilized lookup tables for embeddings. This model does not rely on such methods.

Critique of Current Practices

The speaker expresses skepticism towards data augmentation techniques currently in use, indicating they may not be necessary or effective at this stage.

There’s an acknowledgment that while the ideas presented are untested, they could lead to significant insights if explored further.

Community Reactions and Personal Background

The speaker notes that visual representations in their findings might be misleading due to ongoing validation processes by other researchers.

An invitation is extended to a guest for further discussion; initial reactions indicate mixed feedback from the community regarding recent work.

Guest Introduction and Academic Journey

The guest shares feelings of being overwhelmed by public reception—both criticism and support—of their work.

They draw parallels between current controversies surrounding test time training and past debates over similar methodologies in research communities.

Transitioning from Academia to Industry

The guest discusses their academic background in physics and math but expresses disillusionment with academia's focus on publishing rather than practical applications.

After exploring startup opportunities post-graduation, they reflect on their experiences within entrepreneurial environments compared to traditional academic paths.

Exploring AI Projects and Learning Pathways

Initial Interest in AI

The speaker shares their experience of joining a project in their city focused on AI, driven by a strong interest in the field.

They mention having prior experience with CNN architectures but felt out of touch with recent advancements, particularly transformers.

Tackling Challenges in AI

In April, the speaker decided to learn by addressing complex problems, specifically mentioning RKGI and neural cellular automata as initial focuses.

After facing demotivation, they opted to write a blog post summarizing their learning journey over four months.

Background and Skills

The speaker highlights their strong foundation in physics and math as an advantage in understanding AI concepts.

They emphasize that coding in AI is not overly complicated; rather, it involves assembling components effectively.

Methodology Overview

The discussion shifts to the methodology used for tackling RKGI benchmarks, inspired by previous works like compress arc.

The speaker explains how humans adapt hypotheses based on input changes during training, which informed their approach to model updates.

Compression Techniques

They critique existing methods for compressing grids separately and advocate for joint compression as more effective.

The proposed method combines insights from TRM and compress arc while allowing parallel training across multiple grids.

Training Process Insights

The speaker outlines the flow of training starting from data preparation: pairs are converted into sequences with tokens for processing.

They describe using teacher forcing during training similar to LLM pre-training but introduce task-specific embeddings for enhanced learning.

Understanding Token Embeddings and Augmentations

Token Embedding Process

Each token in the input pair receives a uniform embedding, which is derived from the task embedding combined with a 3D rope for each layer of the transformer.

During inference, tokens are sent as prompts similar to an LLM (Large Language Model) to predict outputs auto-regressively. The predictions align closely with ground truth values.

Data Augmentation Techniques

The initial idea involved basic augmentations that yielded modest improvements; however, additional techniques were introduced later. Dihadral augmentations and color variations were added, mirroring methods used by TRM and HRM.

Dihadral augmentations involve rotations and reflections, resulting in eight possible transformations including the original configuration. Color permutations add complexity with 100 variations instead of two million to manage memory constraints effectively.

AIVR Methodology

After applying augmentations, AIVR (Augmented Inference via Reverse Transformation) is utilized to generate outputs based on augmented grids. This method aligns with practices from other RKG (Reinforcement Knowledge Graph) methodologies like TRM and HRM.

For each augmented grid output, reverse transformations are applied to consolidate results into 800 potential answers, from which the top two most frequent responses are selected for submission. This approach was critiqued despite its effectiveness due to inherent biases it may introduce in model training processes.

Critique of Current Practices

There’s a recognition that while these methods yield results, they also encode certain biases regarding puzzle-solving boundaries within models—raising questions about fairness in how information is presented during training sessions. Discussions highlight concerns over whether such limitations should be imposed on models or if they can learn more flexibly without explicit guidance on boundaries or structures like grids.

Sample Efficiency Challenges

The speaker expresses interest in RKGI (Reinforcement Knowledge Graph Inference) as a benchmark due to its difficulty level and its implications for sample efficiency—a critical issue in machine learning today where vast amounts of data are typically required for training LLMs effectively. They argue that human learning does not rely on such extensive datasets, prompting exploration into improving sample efficiency strategies within ML frameworks.

The conversation touches upon the balance between using data augmentation techniques versus maintaining true sample efficiency when evaluating model performance against new inputs—suggesting that while augmentations can enhance outcomes, they may compromise genuine assessment metrics if not carefully managed.

Data Augmentation and Learning Methods in AI

Overview of Data Augmentation Techniques

The discussion highlights the extensive use of data augmentation, with examples showing up to 300,000 pairs per puzzle, emphasizing its necessity for effective model training.

Previous methods like HRM, TRM, and LMS have utilized similar data augmentation techniques before the current approaches were developed.

Performance on Different AR Models

A question arises about the method's effectiveness with RKGA models; it shows some success with AR2 but not at scale. It is confirmed that AR3 will not work due to its reinforcement learning structure.

The conversation speculates whether new problem formulations could succeed across benchmarks or if large language models (LLMs) will dominate through sheer computational power.

Potential of Large Language Models

The speaker believes LLMs can outperform existing benchmarks like AR1 and R2 given sufficient training data, although they currently lack enough data from ARC.

There’s a strong belief that any task can be automated with enough relevant data, suggesting that transformers are capable of solving complex problems as long as adequate information is available.

Challenges with Reinforcement Learning

The unique challenges posed by AR3 are discussed; it resembles reinforcement learning but presents difficulties in gathering consistent new data due to its ever-changing environment.

Insights on Benchmarking and Pre-training

The competitive nature of improving benchmarks is noted; there’s an ongoing effort to bring various tasks into distribution effectively.

Increasing pre-training data consistently improves model performance as long as the additional data is genuine rather than synthetic.

Compression Techniques in AI Research

A critical view emerges regarding the importance of understanding underlying methodologies rather than being tied to one approach; compression techniques show promise yet remain underexplored.

Discussion points out that while compressing ARC was introduced earlier this year, few researchers pursued this angle despite its potential benefits.

Complexity and Understanding in Compression Methods

The complexity of compression methods may deter researchers from exploring them fully; personal experiences reveal significant time investment required for comprehension.

Surprising Lack of Exploration in Compression Approaches

Despite promising results from compression techniques, there’s confusion over why more researchers haven’t attempted similar approaches; influences from other talks may have sparked interest in these methods.

Reproducibility Concerns

Only a couple of individuals have reproduced the results using provided code; true reproducibility requires independent verification beyond just following existing scripts.

Understanding Joint Self-Supervised Learning and Compression

Overview of Self-Supervised Learning

The speaker explains the concept of supervised learning, where inputs are mapped to outputs, contrasting it with unsupervised learning that lacks labels.

In self-supervised learning, both inputs and outputs are generated together without explicit labels, effectively reproducing the dataset itself.

Large Language Models (LLMs) utilize this method during pre-training by predicting sequences from massive text datasets.

Compression in Self-Supervised Learning

The compression aspect arises as the transformer generates tokens recursively; each token produced serves as input for generating the next one.

The process is termed "joint" because multiple tasks are handled simultaneously rather than sequentially.

Minimum Description Length (MDL)

The discussion shifts to Minimum Description Length (MDL), a principle suggesting that the best model minimizes the length of data description alongside its own model description.

To achieve MDL, moving all information to the output side compresses data effectively; thus, predictions include both inputs and outputs.

Implementation of MDL in Transformers

By predicting inputs too, the overall description length is reduced while maintaining a smaller model size due to fewer parameters needed for weights.

Various theories exist regarding how transformers achieve compression; sequential compression is highlighted as a key mechanism utilized during pre-training.

Comparison with Compressor Architecture

A distinction is made between explicit and implicit MDL implementations: compressor architecture uses Variational Autoencoders (VAEs), which traditionally involve an encoder-decoder structure with a bottleneck for compression.

Compressor architecture innovatively removes the encoder, focusing solely on decoding from latent variables while minimizing information through gradient descent techniques.

Understanding Compression and Intelligence in Model Training

The Role of Compression in Model Performance

The speaker discusses how incentivizing a model to produce additional grid data can lead to accurate outputs, suggesting that "compression is intelligence."

There is confusion regarding the criticism of certain methodologies, as they are similar to existing concepts like compression, which many have not fully explored.

Exploring MDL Objectives in TRM Architecture

The conversation shifts towards the potential benefits of integrating Minimum Description Length (MDL) objectives within Transformer Recurrent Models (TRM).

A suggestion is made to modify TRM by predicting outputs based on latent variables, indicating that this could yield similar results with some adjustments.

Inductive Biases and Their Impact

The discussion highlights the importance of inductive biases such as 3D rope embeddings and task-specific embeddings in enhancing model performance.

It’s proposed that a combination of these factors may be necessary for optimal results, emphasizing the complexity of their interactions.

Generalizability Beyond Specific Tasks

A question arises about whether the discussed methods can be applied beyond specific tasks like RKGI; it’s noted that they are already being utilized for language models (LM).

While acknowledging differences between architectures, it’s suggested that LLM's larger structure might inherently accommodate various enhancements without needing all modifications.

Task-Specific Modifications Required

The speaker reflects on applying these methods to other problems like Sudoku or mazes, noting challenges due to dependencies among tokens.

Emphasis is placed on adapting strategies for each unique task rather than applying a one-size-fits-all approach.

Learning from Test Inputs

An interesting observation is shared about learning from test inputs during problem-solving processes, raising questions about inference limitations.

The dialogue explores how circuits must adapt when encountering new examples during testing, highlighting the need for dynamic modification based on input.

Understanding Latent Updates and Compression in TRM

Latent Updates in TRM

The two methods to modify performance are adjusting weights or altering the latent itself. In TRM, the type of updates is fixed, which can be problematic.

A fixed number of recursion loops (371 layers) may limit adaptability to new problems that require different computational approaches.

The update process for new test inputs should be flexible; fixing the type of latent update could hinder performance on unseen problems.

Compression Techniques

There is a discussion about whether compression alone suffices for benchmarks, with an acknowledgment that current best practices involve transformers as compression algorithms.

While a perfect compressor would theoretically solve many issues, it’s noted that existing compressors outperform transformers in certain contexts.

Application of Compression in LLMs

Compression techniques are already applied in text-based LLMs during pre-training phases, allowing models to learn patterns effectively.

Fine-tuning after pre-training significantly enhances model performance by leveraging learned information.

Architecture Choices and Their Implications

Transformer Architecture for MDL

The choice of a vanilla four-layer transformer is justified as effective for MDL tasks due to ease of training compared to Variational Autoencoders (VA).

Transformers benefit from abundant online data and established methodologies, making them more accessible than VAs which can be challenging to train.

Impact of 3D Rope on Performance

Although ablation studies have not yet removed the 3D rope component, there is an intuition that it significantly aids puzzle-solving capabilities by maintaining symmetry across dimensions.

Initial observations suggest that puzzles solved quickly often involve symmetries or minor modifications, indicating the utility of 3D rope structures.

Scaling Metal Learning: Data vs. Layers

Strategies for Improvement

Scaling metal learning involves three main strategies; however, increasing depth has led to instability during training.

Adding more data has shown clear benefits in performance based on recent ablation studies where removing specific datasets resulted in noticeable drops in effectiveness.

Discussion on Model Scaling and Data Augmentation

The Role of Training Data in Model Performance

The speaker discusses the importance of training data, suggesting that more layers in a model may be beneficial if there is an increase in training data. However, they express uncertainty about the effectiveness of adding layers without additional data.

They mention that increasing the width of their initial transformer model (10 million parameters) significantly improved performance, indicating that width can be more impactful than depth under certain conditions.

Challenges with 3D Rope Implementation

The speaker explains challenges related to implementing 3D rope, noting that it requires dividing dimensions and how adjustments to length can negatively affect outcomes.

They express a desire for collaboration on scaling efforts, highlighting the pressure they feel when working alone and inviting others to provide feedback or corrections.

Peer Review and Community Feedback

The speaker reflects on the peer review process in academic publishing versus receiving immediate feedback from social media platforms like Twitter, which allows for rapid community engagement with their work.

They acknowledge past mistakes made in their original blog post and encourage others to point out errors as part of an open review process.

Issues with Data Augmentation

A question arises regarding performance issues linked to augmentation techniques. The speaker identifies augmentation as a double-edged sword; while necessary for some tasks, it can hinder performance by introducing errors.

They illustrate this point with an example where color augmentation led to misclassification due to incorrect color representation.

Exploring New Approaches to Augmentation

A suggestion is made about scaling augmentations further by creating a generator that produces diverse puzzles while using a compressor to filter out ineffective data points.

The speaker agrees with this idea but emphasizes that models should autonomously determine augmentations rather than relying on human decisions, which could introduce bias.

Concerns About Inductive Biases

There’s concern over how specific augmentations might inadvertently impose biases on model training. For instance, arbitrary changes like color shifts could lead to significant penalties in task performance if not carefully managed.

The discussion concludes with reflections on ensuring that augmentation strategies do not compromise model learning by introducing misleading information or biases.

Discussion on Performance and Augmentation

Challenges with Private Set and Performance

The performance drop in ARK1 is attributed to the complexity of the private set, which is empirically harder to manage. Organizers may be implementing strategies that increase difficulty.

Concerns about Augmentation

There is a belief that removing augmentations could stabilize performance, as they might introduce unnecessary complications or "traps" in the model's learning process.

Compression and Variable Depth

The speaker appreciates the compression approach for simplifying processes. They suggest that variable depth in TRM (Transformer-based models) could enhance understanding of problem-solving through puzzle generation.

Overfitting Issues

The discussion highlights overfitting concerns due to augmentations, where test inputs closely resemble augmented examples, leading to questions about their necessity.

Strategies for Addressing Overfitting

One proposed method involves moving color information into the batch dimension to address specific color-related problems encountered during training.

Exploring Task-Specific Embeddings

Definition and Functionality of Task-Specific Embeddings

Each task has a unique embedding assigned within an embedding space, allowing transformers to recognize task-specific contexts during training by adding these embeddings to token vectors.

Cross-task Learning Mechanism

The goal is for similar tasks' embeddings to converge during training, facilitating cross-task learning. This suggests that tasks can benefit from shared knowledge and improve overall performance.

Comparison with Previous Approaches

Unlike previous methods using separate embeddings for each pair and augmentation, this approach utilizes a single embedding across all pairs, significantly reducing complexity in managing embeddings.

Insights on Task-Based Learning and Data Management in AI

Training Challenges and Methodology

The speaker discusses the complexity of training models with numerous parameters, likening it to a "lookup table," which complicates the training process. This led to a preference for task-based approaches that yielded satisfactory results.

The concept arc was initially added but later removed due to concerns about data leakage from the ARC AGI 2 dataset, which could lead to inflated performance metrics.

The speaker notes that including AR2 tasks in public evaluations would be inappropriate as it risks compromising the integrity of the evaluation by achieving 100% accuracy through data leakage.

Evaluation Strategies

There is a discussion on test time training, emphasizing its necessity for updating prior knowledge based on new inputs. The speaker believes that modifying functions is essential when encountering new data points.

It is reiterated that training must occur either offline or during runtime to ensure accurate outputs when faced with novel inputs, highlighting the importance of adaptability in model training.

Future Directions and Research Plans

After taking a short break, the speaker plans to continue research focused on ablation studies over the next few weeks, indicating an ongoing commitment to refining their approach.

The speaker expresses intentions to submit work related to RKGI after attempting a 50% success threshold first, demonstrating a strategic approach towards evaluation and submission processes.

Conclusion

Gratitude is expressed for sharing insights publicly, reflecting an openness in discussing methodologies and challenges faced during research endeavors.