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[2602.14759] Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training
Summary
The paper presents 'Inner Loop Inference,' a method for enhancing pretrained Transformers by iteratively refining outputs during inference, leading to improved performance on various benchmarks.
Why It Matters
This research addresses the limitations of static pretrained models by proposing a novel approach that allows for dynamic refinement of outputs, potentially unlocking greater capabilities in natural language processing tasks without additional training. It highlights the importance of leveraging existing models more effectively, which is crucial in the rapidly evolving field of AI.
Key Takeaways
- Inner Loop Inference enhances pretrained Transformers without retraining.
- Iterative refinement during inference leads to consistent accuracy improvements.
- The method allows for more stable state evolution and semantic refinement.
- This approach can be applied to various benchmarks in natural language processing.
- It emphasizes the potential of existing models to be utilized more effectively.
Computer Science > Machine Learning arXiv:2602.14759 (cs) [Submitted on 16 Feb 2026] Title:Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training Authors:Jonathan Lys, Vincent Gripon, Bastien Pasdeloup, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene View a PDF of the paper titled Inner Loop Inference for Pretrained Transformers: Unlocking Latent Capabilities Without Training, by Jonathan Lys and 5 other authors View PDF HTML (experimental) Abstract:Deep Learning architectures, and in particular Transformers, are conventionally viewed as a composition of layers. These layers are actually often obtained as the sum of two contributions: a residual path that copies the input and the output of a Transformer block. As a consequence, the inner representations (i.e. the input of these blocks) can be interpreted as iterative refinement of a propagated latent representation. Under this lens, many works suggest that the inner space is shared across layers, meaning that tokens can be decoded at early stages. Mechanistic interpretability even goes further by conjecturing that some layers act as refinement layers. Following this path, we propose inference-time inner looping, which prolongs refinement in pretrained off-the-shelf language models by repeatedly re-applying a selected block range. Across multiple benchmarks, inner looping yields modest but consistent accuracy improvements. Analyses of the resulting latent trajectories suggest m...
