{"id":"ead3f194-fa87-4b0c-b3d9-d91eb594ee66","arxiv_id":"2510.02590","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"MINTO sets bootstrapped targets to the minimum of online and target network estimates, yielding faster stable value learning across online/offline RL and discrete/continuous actions.","lead":"The paper introduces MINTO, a simple change to how target values are computed in reinforcement learning: always take the smaller estimate from the online network and the target network. This aims to keep learning stable like target networks while moving faster like online networks.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Min(online, target) may introduce underestimation when online Q-values are already low, risking conservative policies or instability","rationale":"The reader's weakest assumption matches the load-bearing point exactly. Full-text access does not remove the need for a targeted bias check; the empirical claim of 'no degradation across diverse benchmarks' remains plausible but unverified without isolating the sign of the bias introduced by the min. This moves the verdict from UNVERDICTED to CONDITIONAL pending the proposed diagnostic.","tokens_in":1687,"tokens_out":374,"duration_ms":76561,"concrete_test":"On a small tabular MDP with known optimal Q-values (e.g., 5-state chain or FrozenLake), run 1000-step training episodes using MINTO, standard target network, and online-only bootstrapping; measure mean absolute bias of the learned Q against the true optimal Q at convergence. If MINTO bias is negative and larger in magnitude than the target-network baseline while policy return drops, the underestimation concern is confirmed.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that taking the element-wise minimum between online and target estimates reliably counters the overestimation that would arise from pure online bootstrapping, without creating symmetric underestimation or new sources of instability. In regimes where the online network temporarily underestimates (common early in training or in high-variance continuous-control settings), the min operation lowers the target further than either network alone. This can slow value propagation or bias the policy toward overly conservative actions. The paper's integration into actor-critic methods (e.g., via the same min target for the critic) inherits the same risk, yet the abstract and claimed broad applicability provide no explicit analysis of when the online estimate is the smaller one or how often this occurs across the tested benchmarks.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes MINTO, a simple modification to RL value function updates that sets the bootstrapped target to the element-wise minimum of the online network and target network estimates. The central claim is that this yields faster and more stable learning than standard target networks by reducing overestimation bias from online bootstrapping, while avoiding the instability of pure online targets. The method is presented as easily integrable into value-based and actor-critic algorithms and is evaluated across online/offline RL benchmarks in discrete and continuous action spaces, with consistent reported performance gains.","tokens_in":1838,"tokens_out":515,"duration_ms":110582,"significance":"If the empirical gains hold under rigorous controls, MINTO offers a low-overhead, broadly applicable tweak that could accelerate value learning in many existing RL pipelines. The claimed seamless integration and cross-benchmark consistency are practical strengths, though the absence of analysis on underestimation risks and statistical validation limits the strength of the contribution.","major_comments":[{"comment":"§3 (Method): The description of the min operation does not analyze or bound the cases in which the online estimate is smaller than the target estimate. When this occurs (common early in training or in high-variance settings), the min target can introduce underestimation bias, potentially slowing value propagation or yielding overly conservative policies; this directly affects the central claim that the modification reliably mitigates overestimation without symmetric drawbacks.","section":"§3"},{"comment":"§4 (Experiments): Performance tables and figures report consistent improvements but omit error bars, run counts, statistical significance tests, and details on hyperparameter search or post-hoc selection. Without these, it is impossible to assess whether the gains are robust or could be explained by variance or tuning, which is load-bearing for the broad-applicability conclusion.","section":"§4"}],"minor_comments":[{"comment":"The pseudocode in Algorithm 1 could explicitly annotate the min operation and clarify whether it is applied only to the critic or also affects the actor update.","section":"Algorithm 1"},{"comment":"Notation for online vs. target networks is occasionally inconsistent between text and equations; a single consistent symbol pair would improve readability.","section":null}],"recommendation":"major_revision","confidential_remarks":"The empirical scope is wide but the core idea is incremental; verify whether closely related min-based or conservative target variants appear in the cited literature."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive and detailed feedback. The comments highlight important areas for improving the rigor of our analysis and empirical validation. We address each major comment below and will revise the manuscript accordingly.","responses":[{"response":"We appreciate this point and agree that the current description in §3 lacks explicit discussion of underestimation cases. When the online network produces a lower estimate than the target network, the min operation yields a more conservative target. This can occur early in training or in high-variance environments. However, we argue this is not a symmetric drawback to overestimation: underestimation tends to produce safer, more stable updates that still allow value propagation, whereas overestimation can lead to divergence. Our extensive empirical results across benchmarks show faster convergence without the instability seen in pure online bootstrapping. In the revision, we will expand §3 with a new paragraph analyzing these cases, including conditions favoring underestimation and empirical statistics on how often the online estimate is smaller during training.","revision_made":"yes","referee_comment":"[§3] §3 (Method): The description of the min operation does not analyze or bound the cases in which the online estimate is smaller than the target estimate. When this occurs (common early in training or in high-variance settings), the min target can introduce underestimation bias, potentially slowing value propagation or yielding overly conservative policies; this directly affects the central claim that the modification reliably mitigates overestimation without symmetric drawbacks."},{"response":"We fully agree that stronger statistical reporting is needed to support the broad-applicability claims. The revised manuscript will add error bars (mean ± standard deviation) to all tables and figures, explicitly state that all results are averaged over 5 independent runs with different random seeds, include statistical significance tests (e.g., paired t-tests or Wilcoxon rank-sum tests with p-values reported), and provide a dedicated appendix section detailing the hyperparameter search procedure, ranges explored, and selection criteria. These changes will allow readers to better evaluate the robustness of the reported gains.","revision_made":"yes","referee_comment":"[§4] §4 (Experiments): Performance tables and figures report consistent improvements but omit error bars, run counts, statistical significance tests, and details on hyperparameter search or post-hoc selection. Without these, it is impossible to assess whether the gains are robust or could be explained by variance or tuning, which is load-bearing for the broad-applicability conclusion."}],"tokens_in":1362,"tokens_out":527,"duration_ms":33429,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main contribution is MINTO, which sets the bootstrap target to the element-wise minimum of the online network and the target network. This is presented as a lightweight way to keep the fast-moving targets from online bootstrapping while cutting the overestimation that usually comes with it. The change is easy to drop into existing value-based and actor-critic methods with almost no added cost, and the authors test it across online and offline settings plus discrete and continuous actions.","headline":"MINTO's min(online, target) rule is a simple plug-in that trades some overestimation risk for faster learning, but it can create underestimation when the online network is the smaller one.","tokens_in":2318,"tokens_out":179,"would_cite":false,"duration_ms":27141,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"y = r + γ max_a min(Q¯θ(s', a'), Qθ(s', a')) (Eq. 3); convergence via non-expansion of G_MINTO (Corollary 1, Appendix A)"},{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/RealityFromDistinction.lean","rs_theorem":"reality_from_one_distinction","paper_passage":"MINTO integrated into DQN, IQN, CQL, SAC across discrete/continuous, online/offline settings"}],"headline":"MINTO's min(online, target) target rule is a standard RL stabilization heuristic with no structural overlap to RS forcing chain","alignment":"orthogonal","rationale":"The paper's core machinery (Eq. 3: y = r + γ max_a min(Q¯θ(s',a'), Qθ(s',a'))) is an empirical bias-mitigation operator for bootstrapped targets in off-policy RL. It relies on non-expansion properties for tabular convergence (Corollary 1, referencing Lan et al. 2020) and extensive Atari/MuJoCo benchmarks. RS derives spacetime, c/ℏ/G, φ, J-cost = ½(x + x⁻¹) − 1, and 8-tick periodicity from a single distinction via Universal Forcing (reality_from_one_distinction, AbsoluteFloorClosure, Cost.FunctionalEquation). No ratio symmetry, cosh-cost, ladder, or 8-period structure appears; the min operator is unrelated to J-cost or Alexander duality arguments for D=3. Domain mismatch (deep RL vs. parameter-free physics derivation) yields orthogonal classification.","tokens_in":59416,"confidence":"high","tokens_out":417,"duration_ms":13977,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Using the minimum estimate between online and target networks produces faster and stable value learning in reinforcement learning.","keywords":["reinforcement learning","target networks","value function learning","overestimation bias","deep Q-learning","actor-critic","online and offline RL"],"falsifier":"A controlled comparison on a standard benchmark such as DQN on Atari or SAC on MuJoCo in which MINTO produces slower learning or more unstable training curves than the unmodified target-network baseline.","tokens_in":2597,"feed_emoji":"⚡","tokens_out":602,"duration_ms":38946,"temperature":0.7,"pith_summary":"The paper introduces MINTO, a simple change to how targets are computed when updating value functions. Instead of relying solely on a slowly updated target network or the fast but unstable online network, the target becomes the lower of the two estimates. This targets the overestimation that arises when the online network is used directly for bootstrapping. The approach integrates into many value-based and actor-critic methods at almost no extra cost and shows better results across online and offline settings with both discrete and continuous actions.","feed_headline":"Min of online and target estimates accelerates stable RL","feed_subtitle":"This update rule reduces overestimation bias to reach better performance faster across many reinforcement learning settings.","key_machinery":"The MINTO target rule, which computes each update target as the minimum between the current online network estimate and the target network estimate.","core_discovery":"MINTO sets the bootstrapped target to the minimum of the online network's estimate and the target network's estimate. This yields faster convergence than a fixed target network while avoiding the instability and overestimation that typically occur when the online network serves as the target. The authors report consistent gains when the rule is added to existing algorithms and tested on a wide collection of online RL, offline RL, discrete-action, and continuous-action benchmarks.","pith_inferences":["Similar minimum operations might be worth testing in other learning systems that face a speed-stability tradeoff.","The approach could be examined in larger-scale or real-world control tasks beyond the current benchmarks.","It may be useful to check whether the same rule helps in policy-gradient or model-based methods."],"forward_implications":["MINTO can be added to a broad range of value-based and actor-critic algorithms with negligible overhead.","Value-function updates converge more quickly while preserving stability.","Performance improves across online RL, offline RL, discrete actions, and continuous actions.","The overestimation bias that appears when the online network is used for bootstrapping is reduced."],"fun_headline_variants":["MINTO: Min of online and target estimates for stable RL","MINTO sets target as min of online and target estimate","MINTO uses min of online and target for fast RL","Online and target min estimate stabilizes value learning"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The minimum operation between the two estimates will reduce overestimation bias without creating underestimation or instability in the settings where it is applied.","fun_headline_variants_meta":{"raw":{"variants":["MINTO: Min of online and target estimates for stable RL","MINTO sets target as min of online and target estimate","MINTO uses min of online and target for fast RL","Online and target min estimate stabilizes value learning"]},"model":"grok-4.3","cost_usd":0.011979,"raw_usage":{"total_tokens":5141,"prompt_tokens":645,"num_sources_used":0,"completion_tokens":63,"cost_in_usd_ticks":119790500,"prompt_tokens_details":{"text_tokens":645,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":4433,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":645,"tokens_out":63,"duration_ms":127285,"temperature":1.0,"reasoning_tokens":4433,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-21T21:26:30.131369+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled comparison on a standard benchmark such as DQN on Atari or SAC on MuJoCo in which MINTO produces slower learning or more unstable training curves than the unmodified target-network baseline.","supporting_citations":[],"review_version":1}