{"id":"7ad9f65c-ffa0-4638-9d61-b2746d58bfc3","arxiv_id":"2606.01830","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"CAPF improves Qwen3-4B exact-match scores from 44.7% to 48.5% on seven QA benchmarks by allowing privileged verifier feedback during RLVR training with attenuated credit for the feedback step.","lead":"This paper introduces Credit-Attenuated Privileged Feedback (CAPF) as a training mechanism that uses verifier information to convert failed search-agent rollouts into successful ones during reinforcement learning. A smart generalist might read it to see a practical way to address sparse positive rewards in LLM agent training without changing inference.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Verifier-side error identification may not consistently produce usable repair trajectories, and credit attenuation may fail to fully decouple policy from the privileged call at deployment.","rationale":"The reader's weakest_assumption directly identifies the two conditions required for the headline empirical claim to transfer to deployment; the full-text description does not add independent verification (e.g., machine-checked proof or shipped code) that would remove the risk.","tokens_in":1723,"tokens_out":367,"duration_ms":17902,"concrete_test":"Train two otherwise identical Qwen3-4B runs on the same seven benchmarks: one with full CAPF (feedback + attenuation) and one with feedback but no attenuation. At the end of training, evaluate both policies on the same test sets with the privileged call disabled; if the no-attenuation run drops more than 2 points relative to the attenuated run while the attenuated run stays within 1 point of its training-with-call score, the attenuation step is load-bearing.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that verifier information (errors/omissions in the submitted answer) can be turned into positive-reward repair trajectories inside the rollout, and that attenuating credit assigned to the feedback call (and preceding actions) produces a policy whose behavior at test time matches its behavior when the call is present. If either condition fails—e.g., the feedback merely restates the error without supplying a correctable path, or the policy still conditions on the call despite attenuation—the reported 3.8-point gain on the seven QA benchmarks cannot be attributed to CAPF and will not appear at deployment. The abstract states the mechanism but supplies no quantitative check (ablation removing attenuation, or deployment-without-call curve) that would confirm the attenuation succeeds.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes Credit-Attenuated Privileged Feedback (CAPF) for training LLM search agents under RLVR. Verifier-side information identifying errors or omissions in submitted answers is supplied via a privileged feedback call during training, enabling revision of zero-reward attempts into positive-reward repair trajectories. Credit assigned to the feedback call and preceding actions is attenuated so that the resulting policy can be deployed without the privileged call. The abstract reports that this raises Qwen3-4B average exact-match from 44.7% (outcome-only RLVR) to 48.5% across seven open-domain QA benchmarks.","tokens_in":1869,"tokens_out":376,"duration_ms":22814,"significance":"If the attenuation mechanism succeeds in decoupling policy behavior from the training-time privileged signal, CAPF would offer a concrete way to exploit verifier information that is already available inside RLVR loops without changing the deployment interface. The reported 3.8-point gain on multiple benchmarks is modest but directly addresses the sparse-positive-trajectory problem highlighted in the introduction.","major_comments":[{"comment":"Abstract: the claim that credit attenuation 'accommodates deployment without this call' is load-bearing for the central contribution, yet the manuscript supplies no ablation that removes the attenuation, no deployment-without-call performance curve, and no analysis showing that the learned policy does not condition on the privileged feedback at test time.","section":"Abstract"},{"comment":"Abstract: the reported 3.8-point empirical gain is presented without reference to the number of independent runs, variance across seeds, statistical tests, or comparison against stronger outcome-only RLVR baselines that might already close part of the gap.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. We address the two major comments point by point below.","responses":[{"response":"We acknowledge that the manuscript does not contain the requested ablations, deployment curves, or conditioning analysis. The attenuation mechanism is designed to limit credit propagation from the privileged step, but without explicit verification the deployment claim remains under-supported. We will add these elements in revision: an ablation of attenuated vs. full credit, test-time results without the privileged call, and policy inspection for conditioning on the feedback token.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the claim that credit attenuation 'accommodates deployment without this call' is load-bearing for the central contribution, yet the manuscript supplies no ablation that removes the attenuation, no deployment-without-call performance curve, and no analysis showing that the learned policy does not condition on the privileged feedback at test time."},{"response":"The reported numbers derive from single-run evaluations. We agree this limits interpretability and will revise the manuscript to include multi-seed averages, standard deviations, and statistical tests. Direct comparison remains to the outcome-only RLVR baseline described in the paper; additional stronger baselines can be discussed if space permits but may require new experiments.","revision_made":"partial","referee_comment":"[Abstract] Abstract: the reported 3.8-point empirical gain is presented without reference to the number of independent runs, variance across seeds, statistical tests, or comparison against stronger outcome-only RLVR baselines that might already close part of the gap."}],"tokens_in":1349,"tokens_out":347,"duration_ms":22120,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core idea is straightforward: during RL training for LLM search agents, let the model call a privileged verifier that points out errors in its answer, use that to generate repair trajectories that get positive reward, then down-weight the credit on the call itself and prior steps so the final policy can run without the call. That addresses the real bottleneck that outcome-only RLVR rarely sees successful rollouts on hard questions.\n\nWhat stands out is the explicit mechanism for turning verifier-side information into usable training signal without changing the test-time interface. The paper frames this as a targeted fix rather than a new algorithm, and the reported lift from 44.7% to 48.5% exact match on seven open-domain QA benchmarks with Qwen3-4B is the concrete result.\n\nThe soft spot is that none of the supporting evidence is visible. The abstract states the gain but gives no baselines beyond outcome-only RLVR, no ablation removing the attenuation, no statistical tests, and no curve showing performance when the privileged call is absent at deployment. The stress-test concern lands: if the feedback merely restates the error without supplying a fixable path, or if the policy still leans on the call despite attenuation, the measured improvement will not appear in normal use. Without those checks the attribution to CAPF remains open.\n\nThis is aimed at people already running RLVR on search-augmented agents who need more positive trajectories on difficult items. A reader working on agent training efficiency could extract the mechanism and test it themselves. The work shows clear thinking about the credit-assignment problem in this setting, so it is coherent on its own terms even if the empirical link is thin.\n\nI would send it to peer review. The idea is narrow enough that referees can quickly check whether the experiments close the gap the abstract leaves open.","headline":"CAPF is a training-only privileged feedback trick with credit attenuation to bootstrap positive trajectories in sparse-reward RLVR for search agents, but the abstract supplies no ablations or deployment checks so the 3.8-point claim stays unverified.","tokens_in":2342,"tokens_out":462,"would_cite":false,"duration_ms":14627,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Credit-attenuated privileged feedback during training converts failed LLM search rollouts into successful ones, lifting exact-match scores from 44.7% to 48.5%.","keywords":["search agents","reinforcement learning","privileged feedback","RLVR","LLM agents","open-domain QA","credit attenuation"],"falsifier":"Train two policies on the same data—one with CAPF and one with outcome-only RLVR—then measure whether the CAPF policy still outperforms when both are evaluated without any privileged feedback call.","tokens_in":2631,"feed_emoji":"🔍","tokens_out":652,"duration_ms":23099,"temperature":0.7,"pith_summary":"LLM search agents trained with outcome-only reinforcement learning rarely produce any fully successful trajectories on hard questions, leaving the model with almost no positive learning signal. The paper introduces Credit-Attenuated Privileged Feedback to expose verifier-side information through an extra call available only during training. This information lets the agent revise its own zero-reward answer inside the rollout and receive a positive reward for the repaired trajectory. Credit for the feedback call itself is deliberately reduced so the resulting policy can run without the extra call once deployed. The method is tested on seven open-domain QA benchmarks with the Qwen3-4B model.","feed_headline":"CAPF lifts search-agent QA scores from 44.7% to 48.5% via training feedback","feed_subtitle":"Verifier information creates repair trajectories at train time while credit attenuation keeps the deployed policy unchanged.","key_machinery":"Credit-Attenuated Privileged Feedback (CAPF), a training-only mechanism that inserts verifier information into the rollout to create repair trajectories while lowering credit for the inserted call.","core_discovery":"CAPF supplies verifier-side information via a Privileged Feedback call during training so the policy can revise zero-reward attempts into positive-reward repair trajectories. Credit is attenuated for the feedback call and earlier actions to ensure the learned policy performs well without the call at deployment. On seven open-domain QA benchmarks this raises Qwen3-4B average exact-match score from 44.7% under outcome-only RLVR to 48.5%.","pith_inferences":["Credit attenuation may let other training-time oracles be used without changing the final deployed behavior.","The repair-trajectory idea could apply to any RL setting where partial correctness signals exist at train time but not at test time.","Extending the mechanism to longer multi-hop search tasks would test whether the same credit rule scales."],"forward_implications":["Agents can learn from repaired trajectories instead of waiting for rare end-to-end successes.","The same policy can be deployed without the privileged call because credit for that call was attenuated.","Exact-match performance rises on multiple open-domain QA benchmarks for the tested 4B model."],"fun_headline_variants":["CAPF raises search-agent QA from 44.7% to 48.5% with privileged feedback","Verifier feedback in CAPF improves search-agent QA 44.7% to 48.5%","CAPF with credit-attenuated feedback improves QA from 44.7% to 48.5%","Privileged feedback raises Qwen3-4B QA scores from 44.7% to 48.5% in CAPF"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The verifier can reliably identify errors or omissions and supply guidance that the policy can internalize for use without the verifier later.","fun_headline_variants_meta":{"raw":{"variants":["CAPF raises search-agent QA from 44.7% to 48.5% with privileged feedback","Verifier feedback in CAPF improves search-agent QA 44.7% to 48.5%","CAPF with credit-attenuated feedback improves QA from 44.7% to 48.5%","Privileged feedback raises Qwen3-4B QA scores from 44.7% to 48.5% in CAPF"]},"model":"grok-4.3","cost_usd":0.008989,"raw_usage":{"total_tokens":4032,"prompt_tokens":658,"num_sources_used":0,"completion_tokens":109,"cost_in_usd_ticks":89887000,"prompt_tokens_details":{"text_tokens":658,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3265,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":658,"tokens_out":109,"duration_ms":25225,"temperature":1.0,"reasoning_tokens":3265,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T14:14:11.266334+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Train two policies on the same data—one with CAPF and one with outcome-only RLVR—then measure whether the CAPF policy still outperforms when both are evaluated without any privileged feedback call.","supporting_citations":[],"review_version":1}