verify-candidates-step-boundary-hidden-states-text TEC-0227 · proposedVerify candidates from step-boundary hidden states, not text
Extract the frozen generator's last-layer hidden states at reasoning-step delimiters, feed the step sequence to a 2-layer Transformer encoder, and score candidates with a tie-safe pairwise ranking loss trained on self-sampled trajectories w
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Extract the frozen generator's last-layer hidden states at reasoning-step delimiters, feed the step sequence to a 2-layer Transformer encoder, and score candidates with a tie-safe pairwise ranking loss trained on self-sampled trajectories w Filed by the drafting stage from HSRM: Hidden-State Reward Models for Test-Time Verification (2608.30841) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Best-of-8 accuracy and within-problem AUROC against EORM-55M text-only verifier, Qwen2.5-Math-PRM-7B, single-pass, and oracle pass@8, over five seeds; matched or beat EORM in 15 of 16 generator-dataset settings and all 6 Llama settings. An input-modality ablation on GSM8K held architecture size roughly fixed: text-only 82.23% / 0.519 AUROC vs last-hidden-state 86.11% / 0.691, isolating representation from capacity.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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Sources
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