Convergent World Representations and Divergent Tasks

• URL: http://arxiv.org/abs/2602.00533v1
• Date: Sat, 31 Jan 2026 05:59:15 GMT
• Title: Convergent World Representations and Divergent Tasks
• Authors: Core Francisco Park,
• Abstract summary: We develop a framework clearly separating the underlying world, the data generation process and the resulting model representations.<n>We find that different tasks give rise to qualitatively and quantitatively distinct world representation geometries.<n>To study adaptation, we pretrain models on all tasks, then test whether new entities (cities) can be consistently integrated into the representation space.
• Score: 7.378937711027778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While neural representations are central to modern deep learning, the conditions governing their geometry and their roles in downstream adaptability remain poorly understood. We develop a framework clearly separating the underlying world, the data generation process and the resulting model representations to study these questions in a controlled setup. 5,075 city coordinates define the world and 7 geometric tasks generate the training data for autoregressive training. We find that different tasks give rise to qualitatively and quantitatively distinct world representation geometries. However, multi-task training drives convergence of world representations: models trained on non-overlapping tasks develop aligned geometric representations, providing controlled evidence for the Multitask Scaling Hypothesis of the Platonic Representation Hypothesis. To study adaptation, we pretrain models on all tasks, then test whether new entities (cities) can be consistently integrated into the representation space via fine-tuning. Surprisingly, we find that despite multi-task pretraining, some tasks, which we call divergent, actively harm the representational integration of new entities and harm generalization. Our results show that training on multiple relational tasks reliably produces convergent world representations, but lurking divergent tasks can catastrophically harm new entity integration via fine-tuning.

Related papers

• Contextual Latent World Models for Offline Meta Reinforcement Learning [17.917947576971816]
  We introduce contextual latent world models, which condition latent world models on inferred task representations and train them jointly with the context encoder.<n>This enforces task-conditioned temporal consistency, yielding task representations that capture task-dependent dynamics.<n>Our method learns more expressive task representations and significantly improves generalization to unseen tasks across MuJoCo, Contextual-DeepMind Control, and Meta-World benchmarks.
  arXiv  Detail & Related papers  (2026-03-03T12:45:20Z)
• Revisiting Multi-Task Visual Representation Learning [52.93947931352643]
  We introduce MTV, a principled multi-task visual pretraining framework.<n>We leverage high-capacity "expert" models to synthesize dense, structured pseudo-labels at scale.<n>Our results demonstrate that MTV achieves "best-of-both-worlds" performance.
  arXiv  Detail & Related papers  (2026-01-20T11:59:19Z)
• Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models [13.90532093512575]
  We propose Imagine-then-Plan (textttITP), a unified framework for agent learning via lookahead imagination.<n>We introduce a novel adaptive lookahead mechanism by trading off the ultimate goal and task progress.<n>Our experiments show that textttITP significantly outperforms competitive baselines.
  arXiv  Detail & Related papers  (2026-01-13T19:49:58Z)
• On the Evaluation of Generative Robotic Simulations [35.8253733339539]
  We propose a comprehensive evaluation framework tailored to generative simulations. For single-task quality, we evaluate the realism of the generated task and the completeness of the generated trajectories. For task-level generalization, we assess the zero-shot generalization ability on unseen tasks of a policy trained with multiple generated tasks.
  arXiv  Detail & Related papers  (2024-10-10T17:49:25Z)
• Disentangling Representations through Multi-task Learning [0.0]
  We provide experimental and theoretical results guaranteeing the emergence of disentangled representations in agents that optimally solve classification tasks.<n>We experimentally validate these predictions in RNNs trained to multi-task, which learn disentangled representations in the form of continuous attractors.<n>We find that transformers are particularly suited for disentangling representations, which might explain their unique world understanding abilities.
  arXiv  Detail & Related papers  (2024-07-15T21:32:58Z)
• Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models [83.02797560769285]
  Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data.<n>Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts.
  arXiv  Detail & Related papers  (2024-05-26T13:11:55Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• Leveraging sparse and shared feature activations for disentangled representation learning [112.22699167017471]
  We propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. We validate our approach on six real world distribution shift benchmarks, and different data modalities.
  arXiv  Detail & Related papers  (2023-04-17T01:33:24Z)
• Investigating the role of model-based learning in exploration and transfer [11.652741003589027]
  In this paper, we investigate transfer learning in the context of model-based agents. We find that a model-based approach outperforms controlled model-free baselines for transfer learning. Our results show that intrinsic exploration combined with environment models present a viable direction towards agents that are self-supervised and able to generalize to novel reward functions.
  arXiv  Detail & Related papers  (2023-02-08T11:49:58Z)
• Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors [72.62423312645953]
  Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Current Imitation Learning algorithms often only consider unimodal expert demonstrations and act in a state-action-based setting. Instead, we combine a mixture of movement primitives with a distribution matching objective to learn versatile behaviors that match the expert's behavior and versatility.
  arXiv  Detail & Related papers  (2022-10-17T16:42:59Z)
• Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks [73.63892022944198]
  We present a generic perception architecture named Uni-Perceiver. It processes a variety of modalities and tasks with unified modeling and shared parameters. Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks.
  arXiv  Detail & Related papers  (2021-12-02T18:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.