arXiv AI
When and How Order-Flow Interventions Work After Dealers Adapt
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arXiv Machine Learning
Sep 24
From Reasoning Strings to Partial Orders: Verifier-Certified Rule Transport through Quotient Policy Optimization
The paper introduces Verifier-Certified Rule Transport (VCRT), a method that uses native verifiers to replay adjacent operation pairs and identify commutation certificates or anti-diamonds, thereby distinguishing true logical dependencies from mere serialization choices in reinforcement learning with verifiable rewards. VCRT assigns policy credit based on the total probability mass of each certified orbit and imposes constraints on post-swap consistency, source retention, and policy drift. In leave-one-environment-out transfer experiments across ProofWriter, CLRS, and Lean, VCRT achieves a 77.60% macro pass rate, outperforming the strongest baseline by 13.06 points, with the largest gains observed in Lean.
arXiv AI
Sep 25
ERRAND: Budgeted Maintenance of Agent Memory
ERRAND is a new method for budgeted maintenance of agent memory that treats revalidation of stored knowledge as a priced errand competing for scarce actions. It uses an errand index that is single‑peaked, allowing certainty in either direction to cost nothing, and repairs by writing new versions rather than deleting old ones. In experiments across two drifting tool‑use worlds, ERRAND outperforms non‑oracle policies, achieving up to 10.0 percentage points improvement over eager revalidation while using only 11.0% of steps, and it self‑terminates when no budget is imposed.
arXiv AI
Sep 3
Coverage, Not Targeting: A Structural Regime in Multi-Turn Agent Credit Assignment
The paper argues that in multi‑turn agentic reinforcement learning, credit assignment should be viewed as a coverage problem rather than a targeting problem. It introduces verifier information density (V_d) as a structural metric, showing that terminal‑state verifiers operate in a low‑V_d regime where targeting fails. Experiments on tau^2‑bench, BFCL, and ToolACE‑2‑8B demonstrate that uniformly distributing reward across all turns outperforms sparse, targeted rewards, and that full chain coverage is necessary for optimal performance.
arXiv Machine Learning
Aug 13
When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits
arXiv:2608. 11560v1 Announce Type: new Abstract: Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning.
arXiv AI
Jun 16
Who Drifted: the System or the Judge? Anytime-Valid Attribution in LLM Evaluation Pipelines
arXiv:2606. 15474v1 Announce Type: new Abstract: Continuous evaluation of LLM products relies on a strong LLM judge treated as ground truth: a cheap monitor scores every interaction and a team is paged when the score drifts down.
arXiv Machine Learning
2d ago
Give Up to Get More: Priors, Not Policies, Govern Test-Time Compute Allocation
arXiv:2611.06372v1 Announce Type: new Abstract: When many inputs share a budget of repeated attempts by a large language model (LLM), a common rule gives harder inputs more. On public math, code and...