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arXiv cs.CL
arXiv cs.CL
7/13/2026
HALO: Hybrid Adaptive Latent Reasoning for Language Models

HALO: Hybrid Adaptive Latent Reasoning for Language Models

Short summary

HALO introduces a hybrid adaptive latent-refinement method that combines coarse refinement with selective second-stage refinement on tokens chosen by scoring and monotonic halting. On benchmarks built from MMLU-Pro and GPQA-Diamond, HALO achieves the best overall average among compared methods while using fewer average refinement steps than fixed baselines. The key advantage is better allocation of refinement rather than simply more computation.

  • •HALO combines coarse refinement with selective second-stage latent refinement on scored tokens
  • •Outperforms fixed-1 and fixed-2 baselines on MMLU-Pro and GPQA-Diamond benchmarks
  • •Achieves near fixed-2 accuracy with fewer refinement steps than fixed-1

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Read full article at arXiv cs.CL

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