HALO: Hybrid Adaptive Latent Reasoning for Language Models
Quick Answer
HALO introduces a hybrid adaptive latent-refinement method that enhances frozen pretrained language models with selective refinement, achieving superior performance on MMLU-Pro and GPQA-Diamond benchmarks.
Quick Take
It outperforms fixed refinement methods by optimizing the allocation of refinement steps, resulting in fewer compute costs while maintaining high accuracy.
Key Points
- HALO combines coarse and selective refinement stages for improved efficiency.
- Achieves best average performance on and -Diamond benchmarks.
- Uses fewer refinement steps than fixed-1 and fixed-2 methods.
- Maintains nearly the same token accuracy as fixed-2 with reduced compute.
- Demonstrates that better allocation of refinement is key to performance.
Paper Resources
📖 Reader Mode
~2 min readAbstract:We study how to improve a frozen pretrained language model with a small amount of adaptive extra computation. A simple approach is to add additional refinement steps on top of the backbone hidden states, but fixed extra refinement can be wasteful: a one-step refinement head may be too weak, while forcing a second full-sequence refinement step everywhere can increase compute without improving transfer. We introduce HALO, a hybrid adaptive latent-refinement method that combines a coarse refinement stage with selective second-stage latent refinement on a subset of tokens chosen by token scoring and monotonic token halting. On the main public benchmark comparison built from MMLU-Pro and GPQA-Diamond, HALO achieves the best overall average among the paper-facing methods, outperforming the frozen backbone, fixed-1, and fixed-2. Internal analysis further shows that HALO reaches nearly the same token-accuracy level as fixed-2 while using fewer average applied refine steps than fixed-1 and far fewer than fixed-2. These results suggest that the key advantage is not simply more refinement, but a better allocation of refinement: HALO achieves the strongest paper-facing result while also using less measured controller compute than either fixed baseline.
| Comments: | 15 pages, 4 figures, preprint |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.08775 [cs.CL] |
| (or arXiv:2607.08775v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08775 arXiv-issued DOI via DataCite |
Submission history
From: Micah Zhang [view email]
[v1]
Sun, 3 May 2026 21:34:35 UTC (75 KB)
— Originally published at arxiv.org
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