Scaling Laws for Pre-training Agents and World Models (2411.04434v2)

Abstract: The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when generative learning objectives on offline datasets (pre-training) are used to model an agent's behavior (imitation learning) or their environment (world modeling). This paper characterizes the role of scale in these tasks more precisely. Going beyond the simple intuition that `bigger is better', we show that the same types of power laws found in LLMing also arise in world modeling and imitation learning (e.g. between loss and optimal model size). However, the coefficients of these laws are heavily influenced by the tokenizer, task & architecture -- this has important implications on the optimal sizing of models and data.

• The paper identifies power-law scaling laws in world modeling for embodied agents, similar to large language models, emphasizing the significant influence of tokenization and compression rates on optimal model and data sizes.
• Key findings indicate that varying token compression substantially impacts scaling law coefficients and parameter trade-offs, shifting the focus between increasing model scale versus dataset scale.
• Analysis of Behavior Cloning models reveals less pronounced scaling laws with tokenized observations under realistic compute budgets, contrasting with continuous CNN-encoded observations which show scaling closer to LLMs.

Scaling Laws for Pre-training Agents and World Models

This paper provides a comprehensive examination of scaling laws in the context of embodied agents, specifically focusing on pre-training for imitation learning and world modeling. It pursues a deeper understanding of how scale impacts the performance of these models and attempts to characterize the role of scale with greater precision. The work parallels similar studies in the field of LLMs, translating insights to the domain of embodied AI, and aims to discern the empirical relationships between variables such as model size, dataset size, and compute resources.

Key Contributions

1. Scaling Laws Observed:
   • Power law relationships akin to those in LLMs are identified in world modeling when using tokenized observations and actions. Notably, the optimal model and data sizes can be influenced by the tokenization process itself, with token compression rates playing a significant part. This asserts broader implications for model and data sizing strategies in world modeling.
2. Variation with Token Compression:
   • The paper highlights that as token compression rate changes, there is a consequential impact on the coefficients of scaling laws. Specifically, transitioning from 256 to 540 tokens per image notably shifts the optimal parameters towards larger model sizes, demonstrating that lower compression rates shift focus towards increasing model scale rather than dataset scale.
3. Behavior Cloning (BC) Models:
   • The analysis extends to behavior cloning, where tokenized architectures show less clear scaling laws under realistic compute budgets due to prolonged loss non-saturation. Here, a distinct behavioral pattern emerges when comparing architectures with tokenized observations versus continuous CNN-encoded observations, the latter aligning scaling behavior closer to those identified in LLMs.
4. Experimental Insights:
   • Experimental work includes a suite of models trained on a substantial dataset of human gameplay in a complex video game setting, revealing empirical insights into model behavior under varied scaling regimes. Moreover, the paper successfully demonstrates the predictive utility of the derived scaling laws by extrapolating to larger, more compute-intensive model configurations.

Implications and Future Directions

The findings of this paper hold several implications for future AI developments:

• Model Design and Tokenization:

The paper underscores the critical importance of architecture and tokenization choices in determining scaling efficiency and effectiveness, suggesting future model design should consider these factors meticulously.

• Pre-training Strategy:

Optimal trade-offs between increasing compute, model size, and dataset diversity are emphasized, steering future work in embodied AI towards more balanced approaches to model scaling akin to those seen in LLMs.

• Transferability of LLM Insights:

Successful application of scaling law insights from LLMs indicates potential for broader cross-pollination of methodological advances between LLMs and embodied AI, possibly informing more generalized best practices across AI domains.

• Further Research:

The document also gestures towards the need for exploration into the impacts of data quality and diversity, which remain under-examined but are crucial for understanding scaling impacts holistically. There's also scope to investigate how these scaling laws can predict and improve real-world application performance beyond pre-training.

While the paper provides a robust framework for understanding scaling in agent pre-training, it does highlight its limitations, primarily focusing on pre-training loss without extending thoroughly into downstream task performance metrics. Future research must bridge this gap to inform comprehensive scaling strategies that align model performance both theoretically and practically.