CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training

• URL: http://arxiv.org/abs/2607.01846v1
• Date: Thu, 02 Jul 2026 08:07:56 GMT
• Title: CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training
• Abstract summary: CLAP converts business data into structured SFT samples, decision-preference samples, holdout sets, risk diagnostics, and release-gate records.<n>On five anonymized manufacturing-scenario batches, QLoRA-style LoRA-SFT yields modest average gains.
• Score: 14.51193578486111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents CLAP (Closed-Loop Agent Post-training), a closed-loop method that converts business data into structured SFT samples, decision-preference samples, holdout sets, risk diagnostics, and release-gate records. CLAP combines data validation, target/evidence normalization, reward/KL diagnosis, offline gates, and application-chain replay to decide whether an adapter is suitable for the target application chain. On five anonymized manufacturing-scenario batches, QLoRA-style LoRA-SFT yields modest average gains: overall score increases by 0.0098, pass rate by 0.0240, and evidence accuracy by 0.0280, while hallucination and wrong facts decrease. Yet only 3 of 5 batches improve, some batches regress, and GRPO exposes high KL risks. Application-chain replay further shows that RAG is necessary for factual extraction; under the same 3B backbone and 100 replay cases, an application-RAG-oriented LoRA-SFT adapter improves value, core fields, and answer-evidence doc/page matching over base+RAG, but increases latency. These results support managing domain-agent post-training through an integrated data-training-evaluation-release loop rather than relying on training completion or a single offline score.

Related papers

• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion [46.262619407930266]
  We train a multi-label classifier on a curated gold dataset of 1,207 CVEs from expert MITRE Center for Threat-Informed Defense mappings.<n>The resulting model approximately doubles recall@5 compared with a zero-shot embedding-similarity baseline.<n>We then investigate whether LLM-assisted labeling can extend the gold dataset.
  arXiv  Detail & Related papers  (2026-07-28T10:59:04Z)
• SCOPE-RL: Optimizing Reasoning Paths Before and After Success [15.079558450636592]
  SCOPE-RL is a framework for reinforcement learning with verifiable rewards.<n>It improves average accuracy by up to 11.2 pp over outcome-only GRPO.<n>It reduces reasoning tokens by up to 27.1% over outcome-only GRPO.
  arXiv  Detail & Related papers  (2026-07-13T12:56:28Z)
• When RLVR Shrinks the Reasoning Boundary: Diagnosing Pass@k Inversion [0.0]
  Reinforcement learning with verifiable rewards (RLVR) can improve one-sample accuracy while making a model worse under repeated sampling.<n>We study this pass@k inversion: after training, the policy may solve fewer distinct problems than its base model at large $k$.<n>The failure concentrates on boundary prompts, where the base model contains rare correct trajectories that are recoverable by sampling but too sparse to reliably appear in finite RLVR rollout groups.
  arXiv  Detail & Related papers  (2026-07-12T17:17:18Z)
• VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct [71.22358682581215]
  Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions.<n>We instantiate this as VeriEvol, an iterative framework with two components.<n>On a five-benchmark visual-math suite, scaling evolved SFT data from 10K to 250K samples raises the mean accuracy from 35.42 to 54.73.
  arXiv  Detail & Related papers  (2026-06-22T16:17:30Z)
• Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning [60.83841532299041]
  We propose Adaptive Data Scheduling (ADS), a dual-level data scheduling framework for pacing reinforcement learning post-training.<n>ADS organizes samples according to semantic patterns and maintains an adaptive inter-cluster distribution to solidify current training progress.<n> Experimental results across three LLMs and seven reasoning benchmarks demonstrate that ADS improves average accuracy by 5.2% over Group Relative Policy Optimization.
  arXiv  Detail & Related papers  (2026-06-21T02:19:17Z)
• DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification [7.917135080399156]
  We propose DecomposeRL as an accurate claim-verifier that produce inspectable traces.<n>DecomposeRL frames decomposition as an RL policy trained with GRPO.<n>We show that a DecomposeRL-7B policy trained with full supervision on only 5K curated claims achieves 86.3 in-domain and 69.8 out-of-domain balanced accuracy.
  arXiv  Detail & Related papers  (2026-05-27T02:19:52Z)
• Feedback-Normalized Developer Memory for Reinforcement-Learning Coding Agents: A Safety-Gated MCP Architecture [0.0]
  This paper presents RL Developer Memory, a local-first, Model Context Protocol (MCP)-native developer-memory architecture for RL coding agents.<n>It treats memory selection as a logged contextual decision process.<n>System is evaluated on a deterministic 200-case benchmark with RL algorithm bugs, hard negatives, review-gated RL/control cases, and low-risk failures.
  arXiv  Detail & Related papers  (2026-05-02T18:37:36Z)
• Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization [74.91418266859297]
  Implicit process reward models (PRMs) provide fine-grained reward signals along the reasoning process.<n>PRMs suffer from a train-inference mismatch: training only constrains a sequence-level aggregate, whereas inference requires token-level scores to reflect local step quality.<n>We propose a novel Implicit Prefix-Value Reward Model (IPVRM), which directly learns a prefix-conditioned value function estimating the probability of eventual correctness.<n>We also propose Distribution-Level RL (DistRL), which computes TD advantages for both sampled tokens and high-probability candidate tokens.
  arXiv  Detail & Related papers  (2026-04-14T18:19:54Z)
• LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications [51.56484100374058]
  We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow.<n>The system combines automated benchmarks, OpenTelemetry observability, and CI quality gates under a minimal API contract.<n>We evaluate the harness on ticket-routing and BEIR tasks with full Azure matrix coverage.
  arXiv  Detail & Related papers  (2026-03-28T18:03:32Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Double Check My Desired Return: Transformer with Target Alignment for Offline Reinforcement Learning [64.6334337560557]
  Reinforcement learning via supervised learning (RvS) frames offline RL as a sequence modeling task.<n>Decision Transformer (DT) struggles to reliably align the actual achieved returns with specified target returns.<n>We propose Doctor, a novel approach that Double Checks the Transformer with target alignment for Offline RL.
  arXiv  Detail & Related papers  (2025-08-22T14:30:53Z)
• A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning [43.847168319564844]
  We propose an FL backdoor defense framework, named CLIP-Fed, that utilizes the zero-shot learning capabilities of vision-language pre-training models.<n>Our scheme overcomes the limitations of Non-IID imposed on defense effectiveness by integrating pre-aggregation and post-aggregation defense strategies.
  arXiv  Detail & Related papers  (2025-08-14T03:39:54Z)
• DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection [0.9499594220629591]
  Adrial prompt attacks can significantly alter the reliability of Retrieval-Augmented Generation (RAG) systems.<n>We present a novel method that applies Differential Evolution (DE) to optimize adversarial prompt suffixes for RAG-based question answering.
  arXiv  Detail & Related papers  (2025-07-20T16:48:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.