OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search

Erhan Zhang; Jiaxin Mao; Wei Yang; Xiaochi Wei; Yan Gao; Yao Hu; Yiqun Chen; Yi Wu; Zechun Niu

REVIEW 3 major objections 3 minor 1 cited by

OASES co-trains a search agent and an outcome-aligned state evaluator so intermediate search steps get rewards that track final answer success.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.5

2026-07-13 12:32 UTC pith:2RYTZS34

load-bearing objection Solid within-subfield RLVR recipe for agentic search, but abstract-only so the load-bearing alignment claim is still uncheckable. the 3 major comments →

arxiv 2604.03675 v3 pith:2RYTZS34 submitted 2026-04-04 cs.AI cs.CLcs.IR

OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search

Erhan Zhang , Yiqun Chen , Zechun Niu , Wei Yang , Xiaochi Wei , Yan Gao , Yi Wu , Yao Hu

show 1 more author

Jiaxin Mao

This is my paper

classification cs.AI cs.CLcs.IR

keywords agentic searchprocess rewardsoutcome-aligned supervisionsearch-evaluation co-trainingmulti-hop QAreinforcement learninglanguage model agents

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Agentic search lets language models pull external evidence step by step, but training them with only final-answer rewards leaves intermediate search actions under-supervised. Prior denser rewards either use proxies that drift from the true goal or fixed judges that go stale as the search policy improves. OASES instead scores each intermediate search state by how well it supports answering the original question, then jointly trains the search policy and that state evaluator so the evaluator keeps pace with changing search behavior. On five multi-hop QA benchmarks the method beats strong reinforcement-learning baselines, and ablations attribute the gains to the outcome-aligned process rewards and the co-training loop. A sympathetic reader cares because reliable process supervision is what lets multi-step search agents improve without collapsing into sparse-reward noise or proxy gaming.

Core claim

Outcome-aligned process rewards—scores of how well each intermediate search state supports answering the original question—combined with co-training of the search policy and the state evaluator, produce more reliable credit assignment for agentic search and outperform strong RL baselines on multi-hop QA.

What carries the argument

Outcome-aligned state evaluation: each intermediate search state is scored by how well it supports answering the original question; the resulting dense rewards supervise the search policy while the evaluator itself is co-trained on-policy so it stays aligned as search behavior evolves.

Load-bearing premise

That scoring an intermediate state by how well it supports the final answer is a faithful dense surrogate for true outcome reward, and that co-training keeps the evaluator aligned rather than letting it become stale or overfit to the policy.

What would settle it

On a multi-hop QA suite, replace the outcome-aligned state scores with either pure outcome-only rewards or a fixed off-policy evaluator; if the performance gap reported for OASES disappears, the central claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

Multi-hop search agents can receive dense credit for intermediate retrieval steps without relying on hand-crafted proxy metrics.
As the search policy improves, the co-trained evaluator continues to supply usable process rewards instead of going stale.
Training loops that jointly update policy and evaluator become a practical alternative to fixed external judges for agentic search.
Gains on five multi-hop QA benchmarks indicate the approach generalizes across common knowledge-intensive evaluation settings.

Where Pith is reading between the lines

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

The same outcome-aligned co-training pattern could densify supervision for other multi-step tool-use agents (code interpreters, web browsers) whose only natural reward is final task success.
If the evaluator’s notion of “supports the answer” can be made cheaper than full trajectory rollouts, process supervision cost may drop enough for larger-scale agent training.
A natural stress test is whether the co-trained evaluator still tracks true outcome quality after the policy distribution shifts dramatically, e.g., under domain transfer.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

Solid within-subfield RLVR recipe for agentic search, but abstract-only so the load-bearing alignment claim is still uncheckable.

read the letter

The one thing to know: OASES is a training recipe that densifies multi-step search supervision by scoring intermediate states on how well they support the original question, then co-trains the search policy and that evaluator so the judge tracks the evolving policy. The abstract claims consistent gains over strong RL baselines on five multi-hop QA benchmarks and attributes them to that combination.

What is new is the pairing of outcome-aligned intermediate scoring with joint search–evaluator co-training, aimed at the two failure modes the authors name: proxy rewards that drift from final answer correctness, and fixed external evaluators that go stale. That is a clean, practical framing for process rewards in tool-using agents. If the full paper delivers the equations, ablations, and variance numbers, this is useful method work for people training multi-hop search agents under RLVR.

Soft spots are real but proportional to what we have. We only have the abstract. There is no definition of the intermediate scoring function, no formal link (bound, estimator, or even reported correlation) between state score and final verifiable reward, and no tables. The stress-test concern is fair on that basis: the claim that this densification stays outcome-aligned rather than reintroducing a learned proxy, and that co-training prevents collapse or policy-overfitting, is load-bearing and currently only asserted. Circularity is moderate—same outcome labels feed both final reward and evaluator training—but the design intent is explicitly to stay tied to the original question, so I would not call it fatal without seeing the method section. Free parameters and the invented evaluator entity are expected for this class of work; they are not red flags by themselves.

Who it is for: people building or training agentic search / multi-hop QA systems who already care about process rewards under RLVR. Not a paradigm shift; a concrete recipe. It deserves a serious referee if the full paper has the usual empirical package (baselines, ablations, metrics). I would not desk-reject on the abstract alone. Bring it to reading group only once the PDF is up; until then, treat the gains as provisional.

Recommendation: accept for peer review when the full manuscript arrives; do not treat the abstract claims as settled.

Referee Report

3 major / 3 minor

Summary. The manuscript proposes OASES, an Outcome-Aligned Search-Evaluation Supervision framework for agentic search under RL with verifiable rewards. It argues that outcome-only rewards are sparse and that existing process rewards either deviate from the final objective (proxy signals) or become stale (fixed evaluators). OASES instead densifies supervision by scoring each intermediate search state according to how well it supports answering the original question, and co-trains the search policy with this state evaluator on-policy so the evaluator tracks evolving search behavior. The abstract reports consistent outperformance over strong RL baselines on five multi-hop QA benchmarks, with analyses said to confirm the value of outcome-aligned process rewards and search-evaluation co-training.

Significance. If the claimed mechanism is correctly specified and the empirical gains hold under rigorous comparison, OASES would address a genuine bottleneck in agentic search: sparse outcome credit assignment without introducing proxy-deviation or stale process judges. Outcome-aligned intermediate scoring plus on-policy co-training is a coherent design response to the failure modes the abstract attributes to prior process-reward methods, and multi-hop QA is an appropriate testbed. The contribution would be of clear interest to the RL-for-agents and retrieval-augmented generation communities. At present, however, only the abstract is available, so significance remains conditional on definitions, formal links to outcome reward, and reproducible experimental evidence that are not yet checkable.

major comments (3)

The central claim that intermediate-state scores of “how well this state supports answering the original question” constitute outcome-aligned (non-proxy) process rewards is load-bearing for the paper’s reliability argument, yet the abstract only asserts it. No scoring function, estimator, lower-bound, or correlation guarantee relating intermediate scores to the final verifiable outcome is stated. Without that link, densification may reintroduce the proxy-deviation problem the work sets out to fix. A formal definition and a concrete alignment check (e.g., correlation or regret relative to outcome) are required before the claim can be assessed.
The co-training claim—that joint on-policy training of policy and evaluator keeps process supervision reliable as search behavior evolves—is likewise load-bearing and only asserted. The abstract does not specify the co-training objective, update schedule, data mixture, or any safeguard against evaluator collapse or policy-overfitting. Absent these details (and ablations that isolate co-training from a fixed or offline evaluator), gains cannot be attributed to adaptive alignment rather than other training factors.
The empirical claim of consistent outperformance on five multi-hop QA benchmarks cannot be verified from the abstract alone: no baselines, metrics, absolute/relative deltas, variance, or failure cases are reported. For a methods paper whose contribution is process-supervision reliability, the main results table and the ablations that “confirm” outcome-aligned rewards and co-training must be inspectable; otherwise the central performance claim remains uncheckable.

minor comments (3)

The abstract uses “Outcome-Aligned Search-Evaluation Supervision” and “search-evaluation co-training” somewhat interchangeably; a single consistent name for the framework and for the co-training procedure would improve clarity.
“Strong RL baselines” is underspecified in the abstract; naming the principal baselines (and whether they include prior process-reward methods) would help readers place the contribution.
The phrase “evaluating how well each intermediate search state supports answering the original question” should eventually be tied to a concrete implementation (learned judge, heuristic, likelihood, etc.) so that reproducibility is possible.

Circularity Check

0 steps flagged

Abstract-only review: no equations, definitions, or self-citation chain available to exhibit constructional circularity.

full rationale

Only the abstract is available; the full text, equations, training objectives, and citations are not. Circularity analysis requires quoting specific paper text and exhibiting a reduction (e.g., Eq. X equals Eq. Y by construction, or a fitted parameter renamed as a prediction). The abstract describes OASES as deriving process rewards by scoring how well intermediate search states support answering the original question, then co-training the search policy and state evaluator so the evaluator adapts. It claims this yields outcome-aligned densified supervision and outperforms RL baselines on multi-hop QA. These are methodological claims, not demonstrated self-definitional identities or fitted-input-as-prediction steps that can be reduced from the given text. No uniqueness theorem, ansatz via self-citation, or renaming of a known empirical pattern is stated. The reader's concern that intermediate scores may still be proxies trained on the same outcome labels is a correctness/assumption risk, not an exhibited circularity under the hard rules (no speculation; only quote-and-reduce). With no load-bearing derivation chain visible, the honest finding is no significant circularity. Score 0; steps empty.

Axiom & Free-Parameter Ledger

1 free parameters · 3 axioms · 1 invented entities

Abstract-only ledger: free parameters and formal axioms of the reward/co-training objectives are not specified. The claim rests on standard RLVR practice, the domain assumption that intermediate ‘answer-support’ scores densify supervision without reintroducing proxy bias, and the invented co-trained state evaluator as the mechanism that stays non-stale.

free parameters (1)

RL and co-training hyperparameters (unspecified)
Abstract does not list learning rates, reward scales, mixing coefficients between outcome and process rewards, or co-training schedules; any such knobs would be free parameters of the method as usually implemented.

axioms (3)

domain assumption Reinforcement learning with verifiable final-answer rewards is a valid primary training signal for multi-step search agents.
Abstract takes RLVR as the established paradigm and builds process densification on top of it.
ad hoc to paper An intermediate search state’s usefulness can be scored by how well it supports answering the original question, and that score is sufficiently aligned with final outcome to serve as process reward.
This is the paper’s core modeling choice for ‘outcome-aligned’ process rewards; not a standard math fact and not independently verified in the abstract.
ad hoc to paper Co-training the evaluator on-policy with the search agent keeps process supervision reliable as the policy evolves.
Stated as the fix for stale fixed evaluators; load-bearing for the co-training half of the claim.

invented entities (1)

OASES outcome-aligned state evaluator (co-trained with search policy) no independent evidence
purpose: Produce dense process rewards for intermediate search states that stay aligned with final answer outcomes and adapt as the search policy changes.
The evaluator is the new trainable component introduced to replace fixed/proxy process judges; independent evidence outside this paper is not provided in the abstract.

pith-pipeline@v1.1.0-grok45 · 6138 in / 2630 out tokens · 26295 ms · 2026-07-13T12:32:46.323336+00:00 · methodology

0 comments

read the original abstract

Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforcement learning with verifiable rewards (RLVR) has emerged as a widely adopted training paradigm for search agents, yet outcome-only rewards are sparse and provide limited credit assignment for intermediate search actions. Existing process-reward methods therefore seek to densify supervision through proxy signals, external evaluators, or likelihood-based information gain. However, proxy rewards can deviate from the final outcome objective, while fixed evaluators can become stale as the search policy evolves, leading to unreliable process supervision. To address these challenges, we propose OASES, an Outcome-Aligned Search-Evaluation Supervision framework for agentic search. OASES derives outcome-aligned process rewards by evaluating how well each intermediate search state supports answering the original question. It further co-trains the search policy and the state evaluator on policy, allowing the evaluator to adapt to evolving search behavior and provide more reliable process rewards. Experiments on five multi-hop QA benchmarks show that OASES consistently outperforms strong RL baselines, with further analyses confirming the benefits of outcome-aligned process rewards and search-evaluation co-training.

Figures

Figures reproduced from arXiv: 2604.03675 by Erhan Zhang, Jiaxin Mao, Wei Yang, Xiaochi Wei, Yan Gao, Yao Hu, Yiqun Chen, Yi Wu, Zechun Niu.

**Figure 1.** Figure 1: Overview of PRAISE. Left: Main Search Rollout. The policy performs multi-turn search and produces a complete trajectory with a final answer. Middle: Prefix Answering. PRAISE extracts prefix states and generates an intermediate answer from each prefix. Right: Reward Assignment and Joint optimization. Prefix answers are scored against the ground-truth answer, step rewards are computed from adjacent score dif… view at source ↗

**Figure 2.** Figure 2: Step-wise analysis of the prefix evaluator under different optimization strategies. Panels (a)–(c) show the [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Effect of the process-reward weight α under different model sizes and evaluation metrics. None denotes the variant without prefix evaluator. The settings 0–1.0 correspond to different values of the process-reward weight α. A larger α assigns a higher weight to the process reward relative to the final reward. the policy model itself, a frozen Qwen2.5-7B, or a frozen Qwen2.5-14B as the evaluator. w/o proces… view at source ↗

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