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Context and Diversity Matter: The Emergence of In-Context Learning in World Models

Fan Wang, Zhiyuan Chen, Yuxuan Zhong, Sunjian Zheng, Pengtao Shao, Bo Yu, Shaoshan Liu, Jianan Wang, Ning Ding, Yang Cao, Yu Kang

TL;DR

These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments.

Abstract

The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches rest on static world models that falter when confronted with novel or rare configurations. We investigate in-context learning (ICL) of world models, shifting attention from zero-shot performance to the growth and asymptotic limits of the world model. Our contributions are three-fold: (1) we formalize ICL of a world model and identify two core mechanisms: environment recognition (ER) and environment learning (EL); (2) we derive error upper-bounds for both mechanisms that expose how the mechanisms emerge; and (3) we empirically confirm that distinct ICL mechanisms exist in the world model, and we further investigate how data distribution and model architecture affect ICL in a manner consistent with theory. These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments.

Context and Diversity Matter: The Emergence of In-Context Learning in World Models

TL;DR

These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments.

Abstract

The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches rest on static world models that falter when confronted with novel or rare configurations. We investigate in-context learning (ICL) of world models, shifting attention from zero-shot performance to the growth and asymptotic limits of the world model. Our contributions are three-fold: (1) we formalize ICL of a world model and identify two core mechanisms: environment recognition (ER) and environment learning (EL); (2) we derive error upper-bounds for both mechanisms that expose how the mechanisms emerge; and (3) we empirically confirm that distinct ICL mechanisms exist in the world model, and we further investigate how data distribution and model architecture affect ICL in a manner consistent with theory. These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments.

Paper Structure

This paper contains 20 sections, 1 theorem, 15 equations, 14 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dynamic Prediction and World Models
  4. In-Context Learning and Meta-Learning
  5. Methodologies
  6. Environment Recognition (ER)
  7. Environment Learning (EL)
  8. Theoretical Analyses of ER and EL
  9. L2World: Long-Context and Linear-Attention World Models
  10. Experiments
  11. Random Cart-Poles
  12. Navigation
  13. Conclusions and Limitations
  14. Assumptions and Proofs for \ref{['theorem:1']}
  15. Additional Experiment Settings
  16. ...and 5 more sections

Key Result

Theorem 1

For Environment Recognition and Environment Learning whose predictive models $\hat{p}_{ER/EL}$ have been sufficiently optimized on the training environments $\mathcal{E}$, the upper bound of the total-variation (TV) distance between the predicted and the ground-truth transition, given a context $C_T

Figures (14)

  • Figure 1: The world model structure for the empirical study.
  • Figure 2: Comparison of models trained on different datasets (color-coded) in Cart-Poles. Performance varies markedly with the training data, revealing distinct tendencies toward ER, EL, or an inability to perform ICL.
  • Figure 3: Best Matching Error (BME) versus prediction error for various models across 130 test cart-poles.
  • Figure 4: Comparison of k-step autoregressive PSNR in Mazes(Unseen).
  • Figure 5: The decline in performance of EL (trained with Maze-32K-L) and ER (trained with Maze-128-L) when observations in contexts are shuffled, measured by PSNR.
  • ...and 9 more figures

Theorems & Definitions (1)

  • Theorem 1