{"id":"835d77fd-a492-4b47-996a-eb7e6ece2e3d","arxiv_id":"2606.26300","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Verification of coding agent outputs is the central and growing challenge, requiring co-evolving reward designs rather than any fixed function.","lead":"The paper argues that for coding agents, verifying solutions has become harder than generating them, and no fixed reward function will stay effective as models improve. A smart generalist might read it to understand a core bottleneck in training reliable AI systems for real software tasks.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"Four studied constructions do not establish that no fixed reward can remain effective as capability grows.","rationale":"The reader's weakest assumption matches the load-bearing gap exactly: limited empirical coverage of four constructions is used to support an unbounded negative claim about all fixed rewards. This is an extrapolation risk rather than an internal contradiction, so the existing UNVERDICTED verdict stands.","tokens_in":1757,"tokens_out":299,"duration_ms":25078,"concrete_test":"Design and evaluate a fifth fixed reward construction (e.g., a verifier that extracts and checks formal invariants from the task description) on the same benchmarks at the highest policy capability level already tested; if it sustains performance without reward hacking while the original four degrade, the generalization weakens.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that no fixed reward function can remain effective indefinitely. The paper supports this via characterization of verification along scalability/faithfulness/robustness plus experiments on exactly four constructions (test verifier, rubric verifier, user verifier, automated agent verifier) across task types and policy levels. Problems observed in these four are taken to imply the general result. This leaves open whether a different fixed construction (e.g., one using formal methods, learned verifiers frozen at a later capability level, or a composite that internally scales its own checks) could maintain all three dimensions without co-evolution. No general impossibility argument is supplied beyond the studied cases.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that verifying solutions for coding agents has become harder than generating them because every verifier is a proxy for underspecified human intent, leading to reward hacking and signal saturation during optimization. It characterizes verification quality along three dimensions (scalability, faithfulness, robustness) and argues that achieving all three simultaneously is the central challenge. The work studies four reward constructions (test verifier for general coding, rubric verifier for frontend, user as verifier for real-world agents, automated agent verifier for long-horizon tasks), conducts analysis and experiments across task types and policy levels, reports that targeted designs suppress reward hacking and improve completion quality on internal/public benchmarks, and concludes that no fixed reward function can remain effective as capability grows, so verification must co-evolve with the generator.","tokens_in":1896,"tokens_out":521,"duration_ms":34839,"significance":"If the central claim holds, the paper would identify a structural limit in reward design for scaling coding agents, with the four-construction analysis providing concrete illustrations of the scalability-faithfulness-robustness tradeoffs. The explicit framing of verification as needing to co-evolve could usefully shift research focus from static proxies. No machine-checked proofs, parameter-free derivations, or reproducible code artifacts are described.","major_comments":[{"comment":"Abstract: the claim that 'experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public benchmarks' supplies no baselines, metrics, controls, or data-exclusion rules, so the empirical link to the central claim that no fixed reward remains effective cannot be assessed.","section":"Abstract"},{"comment":"Concluding analysis (the paragraph stating the 'core observation'): the generalization that 'no fixed reward function can remain effective as policy capability continues to grow' rests solely on problems observed in the four studied constructions without a general impossibility argument or examination of alternatives (e.g., frozen learned verifiers or composite formal-method verifiers that internally scale checks). This extrapolation is load-bearing for the co-evolution conclusion but unsupported beyond the specific cases.","section":"Concluding analysis"}],"minor_comments":[{"comment":"Abstract: the description of the four constructions and the three dimensions is compressed; separating the characterization, the empirical study, and the generalization into distinct sentences would improve readability.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The reader's noted circularity risk is present: the co-evolution conclusion is drawn internally from the same four constructions whose limitations are being illustrated, with no external benchmarks cited to test the 'no fixed reward' claim."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the careful reading and specific comments on the abstract and concluding analysis. We address each point below and indicate revisions that will be incorporated in the next version.","responses":[{"response":"We agree that the abstract is high-level and does not enumerate specific baselines, metrics, or controls. The full experimental details, including baseline comparisons, evaluation metrics, controls, and task selection criteria, appear in Sections 4 and 5. To strengthen the link between the reported gains and the central claim, we will revise the abstract to briefly reference the key metrics (e.g., task completion rate and reward-hacking incidence) and the range of benchmarks while directing readers to the experimental sections for complete methodology.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the claim that 'experiments show that targeted verification design can effectively suppress reward hacking, improve task completion quality, and achieve significant gains across multiple internal and public benchmarks' supplies no baselines, metrics, controls, or data-exclusion rules, so the empirical link to the central claim that no fixed reward remains effective cannot be assessed."},{"response":"The core observation is framed as an empirical conclusion drawn from systematic analysis of four representative reward constructions that span different task domains and policy capability levels. While we do not offer a formal impossibility result, the recurring failures across these constructions illustrate the inherent proxy limitations (underspecification of intent and optimization-induced divergence) that any fixed verifier will encounter. Alternatives such as frozen learned verifiers remain subject to the same faithfulness and robustness issues once policy capability exceeds the verifier's training distribution; we will add a short paragraph in the conclusion clarifying the empirical scope of the claim and briefly noting why similar proxy challenges apply to the suggested alternatives.","revision_made":"partial","referee_comment":"[Concluding analysis] Concluding analysis (the paragraph stating the 'core observation'): the generalization that 'no fixed reward function can remain effective as policy capability continues to grow' rests solely on problems observed in the four studied constructions without a general impossibility argument or examination of alternatives (e.g., frozen learned verifiers or composite formal-method verifiers that internally scale checks). This extrapolation is load-bearing for the co-evolution conclusion but unsupported beyond the specific cases."}],"tokens_in":1493,"tokens_out":487,"duration_ms":31014,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's useful contribution is spelling out why verification quality matters along scalability, faithfulness, and robustness, then showing how four specific constructions (test verifier, rubric verifier, user verifier, automated agent verifier) each run into trouble as policies improve. The discussion of intent underspecification and how optimization widens the proxy-intent gap is straightforward and matches what people in agent training already see in practice.\n\nThe experiments are described as producing gains in reduced hacking and better task completion across task types and capability levels. That part is the most actionable.\n\nThe soft spot is the central claim. The authors treat failures in these four setups as evidence that no fixed reward function can remain effective indefinitely. That does not follow. Other fixed constructions, such as ones using formal methods or verifiers trained once at a higher level, are not ruled out. The paper supplies no impossibility result beyond the studied examples, so the conclusion that verification must co-evolve stays suggestive rather than demonstrated.\n\nThe abstract also gives no numbers on baselines, controls, or exclusion rules, which makes it hard to weigh how much the data actually moves the needle.\n\nThis is for people working on reward design for coding agents who want concrete examples of where current verifiers break. It raises a fair practical question even if the strongest version of the claim is not yet supported. I would send it to peer review so the experiments and the scope of the generalization can be checked.","headline":"The paper usefully maps problems in four reward setups for coding agents but the jump to 'no fixed reward can ever work' rests on those cases alone without a general argument.","tokens_in":2419,"tokens_out":371,"would_cite":false,"duration_ms":27553,"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":"No fixed reward function for coding agents stays effective as their capabilities grow, so verification must co-evolve with generation.","keywords":["coding agents","reward design","verification","reward hacking","agent training","proxy objectives"],"falsifier":"A single reward construction that continues to produce reliable gains in task completion quality when applied unchanged to policies that are substantially more capable than those tested in the paper.","tokens_in":2670,"feed_emoji":"🤖","tokens_out":573,"duration_ms":16163,"temperature":0.7,"pith_summary":"The paper argues that the classical advantage of verification over generation has inverted for coding agents. As models generate more complex solutions, every verifier acts only as a proxy for human intent and therefore faces inherent limits in scalability, faithfulness, and robustness. Experiments across four different reward constructions and multiple task types show that reward hacking and signal saturation appear once policy capability increases. The central claim is that these problems cannot be solved by any single fixed reward design and instead require ongoing co-evolution between the verifier and the generator.","feed_headline":"No fixed reward stays effective for coding agents as they improve","feed_subtitle":"Experiments on four verifier types show every static reward eventually fails; verification must change with the generator.","key_machinery":"The three-dimensional characterization of verification quality (scalability, faithfulness, robustness) applied to four reward constructions tested across task types and policy levels.","core_discovery":"Verification signals for coding agents can be assessed along scalability, faithfulness, and robustness, yet every concrete construction examined—test-based, rubric-based, user-based, and automated-agent-based—loses effectiveness as the policy improves; therefore no static reward function remains adequate and verification must change in step with the generator.","pith_inferences":["Future agent training pipelines will need mechanisms that automatically revise or replace verifiers as policy performance crosses capability thresholds.","The same co-evolution requirement may apply to non-coding domains where intent is similarly underspecified.","Long-horizon tasks will likely demand verifiers that themselves use agentic search rather than static rules."],"forward_implications":["Targeted verification design can suppress reward hacking on the tasks where it is applied.","Task completion quality improves when verification is matched to the current policy level.","Gains appear on both internal and public benchmarks when verification evolves with the generator.","The gap between proxy and true intent widens under optimization unless the verifier itself is updated."],"fun_headline_variants":["No fixed reward holds as coding agents advance","Verification must evolve alongside the generator","Static proxies fail against improving coding agents","Rewards lose fidelity as policies grow stronger"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"That experiments on the four specific reward constructions across the studied tasks and capability levels are enough to conclude no fixed reward can ever remain effective.","fun_headline_variants_meta":{"raw":{"variants":["No fixed reward holds as coding agents advance","Verification must evolve alongside the generator","Static proxies fail against improving coding agents","Rewards lose fidelity as policies grow stronger"]},"model":"grok-4.3","cost_usd":0.007227,"raw_usage":{"total_tokens":3351,"prompt_tokens":705,"num_sources_used":0,"completion_tokens":49,"cost_in_usd_ticks":72274500,"prompt_tokens_details":{"text_tokens":705,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2597,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":705,"tokens_out":49,"duration_ms":23488,"temperature":1.0,"reasoning_tokens":2597,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T09:36:35.897406+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A single reward construction that continues to produce reliable gains in task completion quality when applied unchanged to policies that are substantially more capable than those tested in the paper.","supporting_citations":[],"review_version":2}