Researchers are increasingly recognising the potential of large language models to encode abstract concepts within their learned features. Aaditya Vikram Prasad, Connor Watts, and Jack Merullo, all from Goodfire AI, alongside Dhruvil Gala, Owen Lewis, and Thomas McGrath et al., demonstrate a novel application of these features as a scalable source of supervision for open-ended tasks. Their work addresses the critical problem of hallucination in language models by introducing RLFR, a reinforcement learning pipeline that utilises feature probing to identify and correct uncertain claims. This approach not only significantly reduces hallucination rates, achieving a 58% improvement on Gemma-3-12B-IT, but also offers a pathway towards more interpretable and controllable artificial intelligence systems, representing a paradigm shift in how we leverage model understanding for improved learning.
Researchers have unlocked a new method for reducing inaccuracies in large language models by leveraging internal features that represent concepts like factuality. This work introduces RLFR, or Reinforcement Learning from Feature Rewards, a pipeline that repurposes these internal model features as a scalable reward system for open-ended tasks.
Traditionally, such features have been used for monitoring or steering model behaviour during testing, but this study demonstrates their potential as direct...
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