Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readouts
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arXiv:2607.20519v1 Announce Type: new Abstract: Looped Transformers increase test-time computation by repeatedly applying a shared recurrent block. Learned halting objectives in looped Transformers typically use a single exit distribution both as the inference-time stopping rule and as the training-time weighting of per-depth losses. This entangles exit selection with trajectory formation: the gate not only chooses which recurrent state to use, but also determines how strongly each intermediate…
1Key Takeaways
- arXiv:2607.20519v1 Announce Type: new Abstract: Looped Transformers increase test-time computation by repeatedly applying a shared recurrent block.
- Learned halting objectives in looped Transformers typically use a single exit distribution both as the inference-time stopping rule and as the training-time weighting of per-depth losses.
- This entangles exit selection with trajectory formation: the gate not only chooses which recurrent state to use, but also determines how strongly each intermediate….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2607.20519v1 Announce Type: new Abstract: Looped Transformers increase test-time computation by repeatedly applying a shared recurrent block.
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