Progress from logits: zero-shot phase signals for the planner

Lit slice 2026-08-08 ~04:1xZ (standing allocation), targeted at the #6 rung-(b) escalation routing before its execution reads out. Rung (a) located the self-subgoal failure in single-frame phase estimation — the model plans a valid step of the task, just often the wrong one (~10/60 stage-1 rows phase-offset). This slice asked three questions: does anything published beat self-certainty label-free at inference (lane a); what exists for phase/progress estimation beyond a single frame (lane b); and is MG-Select’s masked-contrast prerequisite actually met for us (lane c — answered in the self-certainty page’s corrected escalation note). Sources: “TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics” (2602.19313) and “ProgVLA: Progress-Aware Robot Manipulation Skill Learning” (2605.28231). Cross-references: test-time selection, runtime plan verification.

TOPReward — ask the logits, not the text

The contribution. Zero-shot task-progress and success rewards read directly from a video VLM’s token logits. Given a trajectory prefix and the task instruction, pose one binary completion query — “Does this trajectory complete the task? True or False” — and take log p("True") as the reward for that prefix; min-max normalize within the episode to get a [0,1] progress curve. No training, no fine-tuning, no reward model: the signal is one logit of a frozen VLM. The prior zero-shot state of the art (GVL) asked the model to generate calibrated progress numbers as text, and that is exactly where it dies on open models — LLMs generate badly calibrated numerals. Reading the logit instead of sampling the number is the whole trick.

The experiments. On ManiRewardBench (130 tasks, 653 episodes, Franka/SO-100/YAM arms) TOPReward on Qwen3-VL-8B reaches 0.947 mean VOC vs GVL’s 0.332; on Open X-Embodiment 0.857 vs 0.194 — GVL is near chance on open-source VLMs while the logit probe is not. Models tested include Molmo2-8B — our trunk family. Downstream: success-detection ROC-AUC 0.654 (GVL 0.519 ≈ chance), and advantage-weighted behavior cloning with its progress curves lifted real SO-100 task success ~43%. One ablation worth remembering: running the query through a chat template halves the VOC — the probe works when it aligns with the raw next-token pretraining objective, which rhymes with our raw prompt-slot rendering.

What transfers. The load-bearing fact for us is what the input is: a video prefix, not a frame. Progress is recoverable zero-shot — on our own trunk family — when the model sees history; our planner pass decodes its subgoal from a single frame, and single-frame phase estimation is precisely the rung-(a) failure mechanism. That makes “condition the planner on history” (or: re-rank candidate subgoals with a TOPReward-style logit probe over recent frames) a nameable, evidence-backed escalation if rung (b) reads no-scorer — at the cost of feeding multiple frames through the planner pass. Caveats: per-episode min-max normalization means no absolute cross-episode scale (per-frame candidate ranking, our use, is unaffected); the paper never selects among language plans, so it is not a scorer competitor to self-certainty — lane (a) stands: nothing published beats SC label-free on open-ended text selection. (RoVer and EVE, surfaced in the same sweep, are trained verifier/reward modules — named escalations under our no-labels-at-inference constraint, not competitors.)

ProgVLA — progress as a training-time reweighter, not an inference signal

The contribution. A 0.1B flow-matching policy that co-trains progress heads (an expectile state-value head, a near-completion success classifier, and a state-action critic, all on the policy’s own context tokens) against a purely temporal remaining-horizon target — then uses their detached predictions only as multiplicative weights on the imitation loss, up-weighting high-advantage, near-completion samples. The heads are never deployed; inference is the plain policy.

The experiments. LIBERO 91.1% average (88.6% long-horizon), beating SmolVLA-2.25B by 2.4 and OpenVLA-7B by 14.6 points at ~20× fewer parameters; Meta-World +7 to +21 on the harder tiers. But the ablation column that matters: removing the progress objectives costs only −2.3 points (concentrated on long-horizon), vs −16.0 for their context resampler and −13.5 for unfreezing vision — the progress machinery is the smallest lever in their own accounting.

What transfers, and what doesn’t. We already co-train aux progress/subgoal heads (that is idea #6’s whole premise, and aux-off cost us +0.462 — a far bigger effect than their −2.3, though ours measures representation shaping, not loss reweighting). Progress-weighted imitation is a training-side idea, off the current inference-rung path; parked. Crucially ProgVLA never ablates single-frame vs history-conditioned progress — it does not unblock lane (b) at inference. The history direction is instead carried by TOPReward above (and by DIM-WAM’s multi-scale historical memory in world-action models — same “history fixes phase” direction, much heavier; one-liner only).

What this fed

• #6 escalation routing (the point of the slice): if rung (b) reads no-scorer (ceiling ≫ bon), two escalations are now mapped with prerequisites checked: (1) masked-contrast selection — prerequisite met, see the corrected note on the self-certainty page: the subgoal-masked reference is our planner-less forward, trained at 50% dropout, N+1 teacher-forced action passes; (2) history-conditioned planning — TOPReward shows phase is zero-shot recoverable from a video prefix on our trunk family; the planner-side fix attacks the measured ~10/60 phase-offset mechanism directly. If rung (b) reads no-diversity, none of this fires and the family closes.
• Lane (a) verdict banked: no published label-free inference-time scorer beats self-certainty on open-ended text — the rung-(b) frozen scorer cell survives this slice unchanged.
• Training-side (parked): progress-weighted imitation (ProgVLA) and TOPReward-style advantage-weighted BC are data-flywheel ideas for a future training rung, not this family.