Word Class Representations Spontaneously Emerge from Successor Representations Trained on Natural Language
arXiv:2605.24585v1 Announce Type: new Abstract: Language models are typically trained to predict the next token in a sequence. Here, we explore an alternative predictive principle from reinforcement learning: Successor Representations (SRs), which model the expected discounted distribution of future states rather than the immediate next state. We transfer this framework to natural language and train neural networks to predict future word distributions across multiple temporal horizons, thereby learning representations of long-range transition structure. We train a deep residual neural network
This research explores fundamental AI learning mechanisms outside the dominant 'next token prediction' paradigm, indicating a maturation of AI research that seeks more efficient and biologically plausible learning. It coincides with increasing efforts to develop more robust and generalized AI models.
This research suggests a more biologically plausible and potentially more efficient way for AI to understand language, leading to models that internalize meaning and relationships rather than just statistical patterns, relevant for sophisticated AI agents.
The research proposes a fundamental shift in how language models learn, moving from immediate next-token prediction to modeling long-range future distributions, which could lead to significantly different and more robust language representations.
- · AI researchers
- · NLP developers
- · Reinforcement learning practitioners
- · Companies building advanced AI agents
- · AI paradigms solely focused on next-token prediction (if SRs prove superior long
New types of language models emerge that derive meaning and 'word classes' through successor representations, potentially improving contextual understanding and reasoning.
Improved language understanding could lead to more effective AI agents capable of higher-level planning and interaction without explicit hard-coded rules.
This could accelerate the development of more generalizable AI that better mimics human cognitive processes for language, impacting a wide range of AI applications from customer service to scientific discovery.
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Read at arXiv cs.CL