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arxiv: 2410.08146 · v1 · pith:EWU7G3DXnew · submitted 2024-10-10 · 💻 cs.LG · cs.CL

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Amrith Setlur , Chirag Nagpal , Adam Fisch , Xinyang Geng , Jacob Eisenstein , Rishabh Agarwal , Alekh Agarwal , Jonathan Berant
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Aviral Kumar
This is my paper

Pith reviewed 2026-05-21 01:37 UTC · model grok-4.3

classification 💻 cs.LG cs.CL
keywords process reward modelsLLM reasoningreinforcement learningtest-time searchprocess advantage verifiersoutcome reward models
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The pith

Process rewards that measure progress under a distinct prover policy outperform outcome rewards for improving LLM reasoning

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that good process rewards should quantify how much a reasoning step changes the likelihood of eventually reaching a correct answer. This change, or progress, is measured under a prover policy that is deliberately kept separate from the base policy being improved. Training process advantage verifiers on these automatically generated progress signals produces dense rewards that support better exploration. The resulting verifiers deliver higher accuracy and lower compute cost than outcome reward models when used for test-time search, and they also improve sample efficiency and final accuracy when used as rewards in online reinforcement learning. The characterization further shows that even weaker prover policies can still strengthen a stronger base policy.

Core claim

The process reward for a step should measure progress as the change in the likelihood of producing a correct response before and after the step, with this progress evaluated under a prover policy distinct from the base policy. Optimizing process rewards from such provers improves exploration during test-time search and online RL. Training process advantage verifiers to predict progress under these provers yields more than 8 percent higher accuracy and 1.5-5 times greater compute efficiency versus outcome reward models in search, plus 5-6 times better sample efficiency and over 6 percent accuracy gain in online RL.

What carries the argument

Progress defined as the change in future success likelihood before and after a step, measured under a prover policy separate from the base policy and used to train process advantage verifiers.

If this is right

  • Test-time search guided by the trained verifiers is more than 8 percent more accurate and 1.5-5 times more compute-efficient than search guided by outcome reward models.
  • Online reinforcement learning with dense rewards from the verifiers achieves 5-6 times better sample efficiency and more than 6 percent higher accuracy than reinforcement learning with outcome rewards.
  • Even weak prover policies can substantially improve stronger base policies when progress is measured under the distinct-prover regime.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach reduces the need for dense human step-by-step labels by relying on automated progress signals from separate policies.
  • Similar progress definitions could be tested in sequential domains beyond language models, such as planning or program synthesis.
  • Combining signals from several provers of varying strength might further stabilize the training targets.

Load-bearing premise

The progress signal measured under a prover policy distinct from the base policy remains a reliable training target even when the prover is weaker than the base policy and likelihood estimates come from similar models.

What would settle it

Training process advantage verifiers using progress signals computed from the base policy itself rather than a distinct prover, then checking whether the reported accuracy and efficiency gains over outcome reward models disappear in both search and RL settings.

read the original abstract

A promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs) that only provide feedback at the final step. However, collecting dense, per-step human labels is not scalable, and training PRMs from automatically-labeled data has thus far led to limited gains. To improve a base policy by running search against a PRM or using it as dense rewards for reinforcement learning (RL), we ask: "How should we design process rewards?". Our key insight is that, to be effective, the process reward for a step should measure progress: a change in the likelihood of producing a correct response in the future, before and after taking the step, corresponding to the notion of step-level advantages in RL. Crucially, this progress should be measured under a prover policy distinct from the base policy. We theoretically characterize the set of good provers and our results show that optimizing process rewards from such provers improves exploration during test-time search and online RL. In fact, our characterization shows that weak prover policies can substantially improve a stronger base policy, which we also observe empirically. We validate our claims by training process advantage verifiers (PAVs) to predict progress under such provers, and show that compared to ORMs, test-time search against PAVs is $>8\%$ more accurate, and $1.5-5\times$ more compute-efficient. Online RL with dense rewards from PAVs enables one of the first results with $5-6\times$ gain in sample efficiency, and $>6\%$ gain in accuracy, over ORMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes measuring process rewards as the change in likelihood of eventual correct completion (progress) under a prover policy distinct from the base policy. It claims this yields process advantage verifiers (PAVs) that improve test-time search and online RL over outcome reward models (ORMs), with reported gains of >8% accuracy, 1.5-5x compute efficiency in search, and 5-6x sample efficiency plus >6% accuracy in RL; it further claims that even weak provers can improve stronger base policies.

Significance. If the central claims hold, the work provides a scalable route to dense, automated process rewards without human step-level labels and shows that separating the prover policy enables useful signals even when the prover is weaker than the base. The efficiency gains and the weak-prover result would be notable contributions to reward design for LLM reasoning.

major comments (2)
  1. [Abstract] Abstract: the empirical claims (>8% accuracy, 1.5-5x efficiency, 5-6x sample efficiency) are presented without error bars, standard deviations across runs, or statistical tests, so the magnitude and reliability of the reported gains over ORMs cannot be assessed.
  2. [Theoretical characterization and experimental sections] Theoretical characterization and experimental sections: the manuscript provides no ablation that isolates the effect of using a prover policy distinct from the base policy (or of using a weaker prover), which is load-bearing for the claim that such separation yields a reliable, non-circular progress signal when both policies belong to the same model family.
minor comments (1)
  1. [§3] The precise definition of the progress signal (difference of likelihoods) and the procedure for estimating those likelihoods should be stated with an explicit equation and a description of the data used for fitting.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comments point by point below, indicating planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the empirical claims (>8% accuracy, 1.5-5x efficiency, 5-6x sample efficiency) are presented without error bars, standard deviations across runs, or statistical tests, so the magnitude and reliability of the reported gains over ORMs cannot be assessed.

    Authors: We agree that the abstract would benefit from greater statistical detail to allow readers to assess the reliability of the reported gains. The underlying experiments were run with multiple random seeds, but error bars and tests were not included in the abstract for space reasons. In the revised manuscript we will expand the abstract and results sections to report standard deviations across runs and include statistical significance tests comparing PAVs against ORMs. revision: yes

  2. Referee: [Theoretical characterization and experimental sections] Theoretical characterization and experimental sections: the manuscript provides no ablation that isolates the effect of using a prover policy distinct from the base policy (or of using a weaker prover), which is load-bearing for the claim that such separation yields a reliable, non-circular progress signal when both policies belong to the same model family.

    Authors: The theoretical characterization derives the precise conditions on the prover policy that guarantee a non-circular progress signal, including the case of a weaker prover from the same model family. The empirical results are consistent with this analysis. We acknowledge, however, that an explicit ablation directly comparing same-policy versus distinct-policy progress signals is absent. We will add this ablation to the experimental section in the revision to isolate the contribution of policy separation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained with distinct prover and empirical validation

full rationale

The paper's core derivation defines process reward as change in likelihood of correct completion under a prover policy explicitly distinct from the base policy, then theoretically characterizes good provers and trains PAVs to predict that signal. This does not reduce to its inputs by construction: the prover is required to be distinct, the characterization is presented as independent theoretical work, and all reported gains (accuracy, efficiency) are measured against ORMs on held-out tasks rather than being forced by fitting the same likelihoods. No self-citation chain, fitted-input-as-prediction, or self-definitional reduction is exhibited in the provided abstract or claims. The setup is externally falsifiable via the reported search and RL experiments.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that likelihood differences under a separate prover constitute a useful dense reward signal without introducing new fitted parameters beyond standard model training.

axioms (1)
  • domain assumption A prover policy distinct from the base policy can be used to compute reliable step-level progress signals.
    Invoked in the key insight paragraph of the abstract.

pith-pipeline@v0.9.0 · 5875 in / 1198 out tokens · 19268 ms · 2026-05-21T01:37:04.343010+00:00 · methodology

discussion (0)

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    the process reward for a step should measure progress: a change in the likelihood of producing a correct response in the future, before and after taking the step, corresponding to the notion of step-level advantages in RL

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Forward citations

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    on the correctness of the full response (ORM). Here, the trained ORMs are mainly used for test-time search (best-of-𝑁). Next, we look at works that alleviate issues with sparse feedback in ORMs, and instead train process reward models (PRMs), that can perform credit assignment. PRMs are trained either through human annotations (Lightman et al., 2023; Uesa...

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    first pit

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    We train for 10,000 iterations in both cases, with a batch size of 64, and a constant learning rate of1𝑒 − 3 for the Adam optimizer. The RL runs are initialized with a supervised finetuned policy. For this we take a randomly initialized network, based on the MADE architecture (Germain et al., 2015), with 3 layers, and 128 hidden units in each. Then we tra...

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    dataset. The finetuning is done for 5000 iterations, with a batchsize of 32, and a maximum learning rate of5𝑒 − 6 for 2B, 9B and 5𝑒 − 7 for the 27B models. We trained the policies using the Adam optimizer, with a linear warm up and cosine decay learning rate schedule. The linear warm up is done for the first 500 iterations. For the base policies, we choos...

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    64k 128k 256k 512k 1024k Training Data Size 0.3 0.4 0.5Accuracy Scaling Laws: First Pit First pit Random Figure 11 | First pit strategy from Luo et al. (2024); Setlur et al. (2024): We compare the beam search performance (with beam size

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    Additional: Experiments on RL Training with PAVs Training details

    E. Additional: Experiments on RL Training with PAVs Training details. As discussed in Section 5, the initialization for RL training is the RFT (rejection finetuned) checkpoint for the corresponding base policies. More specifically, we consider two base policies Gemma 2B SFT, and Gemma 9B SFT, where the RL training is initialized with the policy obtained b...

    work page 2023

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    F.1. Natural Policy Gradient The natural policy gradient (NPG) algorithm (Kakade, 2001a) defines a Fisher information matrix (induced by the policy), and performs gradient updates in the geometry induced by the following matrix: 𝐹𝜌(𝜋) = 𝔼𝒔∼𝑑𝜋 𝜌 𝔼𝑎∼𝜋(· |𝒔) h ∇𝜋 log 𝜋(𝑎 | 𝒔) ∇𝜋 log 𝜋(𝑎 | 𝒔) ⊤i (11) Typically, the NPG update does gradient updates on the obje...

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