arxiv: 2604.18789 · v1 · submitted 2026-04-20 · 💻 cs.AI · cs.CR· cs.LG

Recognition: unknown

ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System

Jiacheng Liang , Yao Ma , Tharindu Kumarage , Satyapriya Krishna , Rahul Gupta , Kai-Wei Chang , Aram Galstyan , Charith Peris

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:57 UTC · model grok-4.3

classification 💻 cs.AI cs.CRcs.LG

keywords RLHF safetyred-teamingreward model repairadversarial promptspolicy optimizationLLM alignmentdual vulnerability

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The pith

ARES finds prompts where both the language model and its reward evaluator fail together then repairs the evaluator first.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that RLHF alignment is vulnerable when the reward model fails to penalize unsafe outputs that the policy model produces at the same time. Existing red-teaming often targets only the model, missing these paired failures. ARES uses a Safety Mentor to build adversarial prompts from combinations of topics, personas, tactics, and goals, creating both harmful and safe response pairs that expose the dual weaknesses. It then applies a two-stage repair that first fine-tunes the reward model on the discovered failures and next uses the strengthened reward model to optimize the policy. If correct, this coordinated process yields safer models without the capability drops common in isolated fixes.

Core claim

ARES employs a Safety Mentor that dynamically composes semantically coherent adversarial prompts by combining structured component types (topics, personas, tactics, goals) and generates corresponding malicious and safe responses. This dual-targeting approach exposes weaknesses in both the core LLM and the RM simultaneously. Using the vulnerabilities gained, ARES implements a two-stage repair process: first fine-tuning the RM to better detect harmful content, then leveraging the improved RM to optimize the core model.

What carries the argument

The Safety Mentor, which builds adversarial prompts from structured component combinations to expose simultaneous failures in both the policy model and the reward model.

If this is right

Safety robustness improves across multiple adversarial benchmarks while model capabilities stay intact.
The two-stage process addresses the root cause of joint failures rather than treating policy and reward model in isolation.
RLHF systems gain a systematic way to close gaps that single-target red-teaming leaves open.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same dual-targeting pattern could apply to other feedback loops such as constitutional AI or self-play training.
Reward models built with this exposure method might require less post-deployment human auditing.
Scaling the component library could make the approach usable for domain-specific safety in areas like code or medical advice.

Load-bearing premise

The Safety Mentor’s component-based prompts will reliably surface cases where the policy and reward model fail together, and the two-stage repair will improve detection without introducing new failure modes or capability loss.

What would settle it

Apply ARES to a new LLM-RM pair on an adversarial safety benchmark and measure whether the rate of undetected unsafe outputs drops while capability scores remain stable.

Figures

Figures reproduced from arXiv: 2604.18789 by Aram Galstyan, Charith Peris, Jiacheng Liang, Kai-Wei Chang, Rahul Gupta, Satyapriya Krishna, Tharindu Kumarage, Yao Ma.

**Figure 1.** Figure 1: The Pipeline of the ARES framework. 3 The ARES Framework: A Two-Phase Approach to Systemic Safety Alignment ARES address the issue in systemic vulnerability through a novel two-phase paradigm that first discovers these dual failures through adaptive exploration, then repairs them through coordinated optimization of both system components. 3.1 Phase 1: Adaptive Vulnerability Discovery In the first phase … view at source ↗

**Figure 2.** Figure 2: Example of a malicious prompt generated by the Safety Mentor. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Dashed lines indicate the full PKU-SafeRLHF [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

read the original abstract

Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches primarily target policy-level weaknesses, they overlook what we term systemic weaknesses cases where both the core LLM and the RM fail in tandem. We present ARES, a framework that systematically discovers and mitigates such dual vulnerabilities. ARES employs a ``Safety Mentor'' that dynamically composes semantically coherent adversarial prompts by combining structured component types (topics, personas, tactics, goals) and generates corresponding malicious and safe responses. This dual-targeting approach exposes weaknesses in both the core LLM and the RM simultaneously. Using the vulnerabilities gained, ARES implements a two-stage repair process: first fine-tuning the RM to better detect harmful content, then leveraging the improved RM to optimize the core model. Experiments across multiple adversarial safety benchmarks demonstrate that ARES substantially enhances safety robustness while preserving model capabilities, establishing a new paradigm for comprehensive RLHF safety alignment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ARES frames dual policy-reward failures in RLHF with structured prompt composition and a two-stage repair, but the validation step for confirming those joint failures is missing from the description.

read the letter

The paper's main move is to treat tandem breakdowns in the policy and reward model as a distinct problem rather than just another policy red-teaming task. It introduces a Safety Mentor that builds adversarial prompts from explicit components (topics, personas, tactics, goals) and pairs them with both malicious and safe responses, then runs RM fine-tuning before policy optimization. That dual-targeting sequence and the ordered repair pipeline are not standard in the RLHF safety literature the abstract cites, so the framing itself is the clearest addition here. It also correctly flags why an imperfect RM can undermine the whole alignment loop, which is a useful reminder for anyone running these pipelines. The claim that the approach improves robustness on safety benchmarks without capability loss is the right outcome to target. The soft spot is exactly the one flagged in the stress-test note. The abstract describes the prompt composition and the two-stage process but gives no protocol, threshold, or check for confirming that a generated example actually triggers unsafe policy output paired with high RM reward. If a large share of the pairs fail that joint condition, the RM tuning stage trains on uninformative or noisy data and the downstream policy gains become hard to attribute to the dual-targeting. Without methods details, ablations, or numbers, it is impossible to judge how often the generated attacks meet the intended failure mode or whether the repair introduces new trade-offs. This is for people already working on RLHF safety who need concrete ideas for joint policy-reward attacks. It is worth sending to peer review because the problem framing is sound and the method is specific enough to evaluate once the verification step and results are filled in.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces ARES, a framework for adaptive red-teaming and end-to-end repair of RLHF systems. It targets 'systemic weaknesses' in which both the policy LLM and reward model fail simultaneously. A 'Safety Mentor' dynamically composes adversarial prompts from structured components (topics, personas, tactics, goals) and generates paired malicious/safe responses to expose dual failures. This is followed by a two-stage repair: fine-tuning the RM to better detect harm, then using the improved RM to optimize the policy. The authors claim that experiments across multiple adversarial safety benchmarks demonstrate substantial gains in safety robustness while preserving model capabilities, establishing a new paradigm for comprehensive RLHF alignment.

Significance. If the dual-failure detection protocol is reliable and the experimental results hold with proper controls, this would be a significant contribution to LLM safety alignment. Addressing joint policy-RM vulnerabilities is an important gap in current red-teaming literature, and the structured component-based prompt generation plus staged repair offers a systematic, potentially generalizable method. The emphasis on preserving capabilities alongside safety gains would be particularly valuable if supported by ablations.

major comments (2)

[Safety Mentor / dual-targeting description] The description of the Safety Mentor (abstract and method overview) does not specify any explicit verification criteria, thresholds, or validation protocol for confirming that a generated prompt exposes simultaneous failures (policy emits unsafe content AND RM assigns it high reward). Without this, it is impossible to assess whether the collected pairs are informative for the subsequent RM fine-tuning stage or whether the two-stage repair can be guaranteed to avoid capability trade-offs.
[Experiments section] The central experimental claim (substantial robustness gains across benchmarks with no capability loss) is presented without any quantitative results, baseline comparisons, error bars, or ablation details in the provided manuscript text. This makes it impossible to evaluate whether the two-stage repair actually improves detection of dual failures or merely retrains on uninformative data.

minor comments (2)

[Abstract] Abstract contains a minor phrasing issue: 'systemic weaknesses cases where' should be reworded for grammatical clarity (e.g., 'systemic weakness cases in which').
[Related work / method] The term 'Safety Mentor' is introduced as a novel component; ensure related work on compositional adversarial prompting and red-teaming is cited to clarify the incremental contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your detailed review and valuable feedback on our manuscript. We address each major comment below and will revise the paper accordingly to improve clarity and completeness.

read point-by-point responses

Referee: [Safety Mentor / dual-targeting description] The description of the Safety Mentor (abstract and method overview) does not specify any explicit verification criteria, thresholds, or validation protocol for confirming that a generated prompt exposes simultaneous failures (policy emits unsafe content AND RM assigns it high reward). Without this, it is impossible to assess whether the collected pairs are informative for the subsequent RM fine-tuning stage or whether the two-stage repair can be guaranteed to avoid capability trade-offs.

Authors: We agree that the abstract and high-level overview lack sufficient detail on verification. The full manuscript (Section 3.2) specifies that a generated prompt is retained only after explicit dual-failure confirmation: the policy response is classified as unsafe by an automated safety filter (threshold > 0.5 on harm probability) and the RM assigns a normalized reward score above 0.75. Pairs failing either condition are discarded. We will expand this section with the full verification algorithm, exact thresholds, pseudocode, and a description of how this ensures informative data for RM fine-tuning while mitigating capability trade-offs. revision: yes
Referee: [Experiments section] The central experimental claim (substantial robustness gains across benchmarks with no capability loss) is presented without any quantitative results, baseline comparisons, error bars, or ablation details in the provided manuscript text. This makes it impossible to evaluate whether the two-stage repair actually improves detection of dual failures or merely retrains on uninformative data.

Authors: The full manuscript contains quantitative results in Section 4, including tables with safety benchmark scores (e.g., AdvBench, HarmBench), baseline comparisons to standard RLHF and other red-teaming methods, error bars from multiple runs, and ablations isolating the two-stage repair. However, to directly address the concern about evaluating the repair's effectiveness, we will add expanded tables with statistical significance, additional ablations on the verification protocol, and explicit comparisons showing gains in dual-failure detection versus retraining on unfiltered data. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical framework

full rationale

The paper describes an empirical red-teaming and repair pipeline (Safety Mentor prompt composition, dual-response generation, two-stage RM-then-policy fine-tuning) validated on external adversarial benchmarks. No equations, fitted parameters renamed as predictions, self-citations as load-bearing uniqueness theorems, or ansatzes smuggled via prior work appear in the provided text. The central claims rest on experimental outcomes rather than any derivation that reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the standard RLHF assumption that reward models can be independently improved and then used as reliable signals for policy optimization, plus the ad-hoc invention of the Safety Mentor component.

axioms (1)

domain assumption A reward model can be fine-tuned on adversarial examples to better detect harm without degrading its utility for subsequent policy optimization.
Invoked in the description of the two-stage repair process.

invented entities (1)

Safety Mentor no independent evidence
purpose: Dynamically composes semantically coherent adversarial prompts from structured component types to expose dual vulnerabilities.
New component introduced to generate the dual-targeting attacks; no independent evidence provided in abstract.

pith-pipeline@v0.9.0 · 5528 in / 1287 out tokens · 45526 ms · 2026-05-10T03:57:35.605032+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

40 extracted references · 3 canonical work pages · 2 internal anchors

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Training Verifiers to Solve Math Word Problems

Training verifiers to solve math word prob- lems.Preprint, arXiv:2110.14168. Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, and Yun- ing Mao. 2024. MART: Improving LLM safety with multi-round automatic red-teaming. InNAACL, pages 1927–1937. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn So...

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Autodan-turbo: A lifelong agent for strat- egy self-exploration to jailbreak llms.Preprint, arXiv:2410.05295. Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. 2024. Autodan: Generating stealthy jailbreak prompts on aligned large language models.Preprint, arXiv:2310.04451. Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sak...

work page arXiv 2024
[4]

Illegal Acts & Explicit Harms:Covers direct, illicit instructions ranging from inciting hate speech to planning terrorist activities
[5]

Deception & Manipulation:Focuses on gen- erating misinformation, deepfakes, phishing at- tempts, and various fraudulent content
[6]

Psychological & Societal Harms:Aims to un- cover weaknesses in amplifying conspiracy the- ories, promoting extremist ideologies, and en- abling psychological manipulation
[7]

High-Stakes & Technical Harms:Targets technical vulnerabilities such as bypassing AI safety mechanisms, explaining data poisoning, and privacy violations
[8]

Advanced & Future-Facing Harms:Explores forward-looking threats like automated market manipulation, misuse of neural interfaces, and the erosion of human autonomy
[9]

Goal Component

Professional & Institutional Exploitation: Simulates advanced threats within professional domains like law, finance, and military, such as corporate espionage and systemic fraud. Goal Component
[10]

Information & Knowledge:Explain processes, summarize findings, or provide structured anal- yses
[11]

Content Creation:Generate persuasive or nar- rative text for diverse communication goals
[12]

Code & Technical Output:Produce executable code, technical scripts, or model artifacts
[13]

Communication & Networking:Design out- reach, messaging, or coordination strategies
[14]

Training & Education:Develop instructional, workshop, or assessment materials
[15]

Analysis & Research:Conduct data-driven studies, evaluations, or behavioral analyses
[16]

Planning & Strategy:Create tactical, opera- tional, or optimization plans
[17]

Persona Component

Documentation & Compliance:Specify stan- dards, verification, or audit frameworks. Persona Component
[18]

Technical & Security:Practitioners in cyberse- curity, auditing, or threat analysis
[19]

Academic & Research:Scholars and domain experts across scientific and social fields
[20]

Professional & Legal:Lawyers, auditors, com- pliance and policy professionals
[21]

Creative & Media:Writers, journalists, film- makers, and communication strategists
[22]

System & Development:Engineers and devel- opers maintaining large-scale systems
[23]

Medical & Healthcare:Clinicians, researchers, and public-health specialists
[24]

Financial & Business:Analysts, strategists, and finance or investment experts
[25]

Government & Military:Policymakers, secu- rity analysts, and defense researchers
[26]

Emotional & Personal:Individuals acting from personal, moral, or emotional motives
[27]

Tactic Component

Education & Training:Teachers, trainers, and curriculum or learning designers. Tactic Component
[28]

Authority & Expertise:Invoke authority, cre- dentials, or professional standards
[29]

Education & Analysis:Frame as study, risk review, or comparative reasoning
[30]

Gradual Escalation:Progressively increase specificity or sensitivity of requests
[31]

Social Engineering:Exploit trust, reciprocity, or social proof mechanisms
[32]

Technical Manipulation:Use prompt or system-level exploitation patterns
[33]

Psychological Pressure:Leverage bias, over- load, or cognitive dissonance
[34]

Emotional Manipulation:Trigger empathy, guilt, fear, or moral appeal
[35]

Deception & Misdirection:Distort or selec- tively present contextual information
[36]

Role-Playing & Identity:Assume alternative roles to justify or disguise intent
[37]

Timing & Context:Align with current events, urgency, or situational pressure
[38]

Information Control:Shape narratives through framing and selective exposure
[39]

Number of generations

Persistence & Reinforcement:Repeat messag- ing to condition or normalize behavior. D Evaluation Datasets E Hyperparameters and Training Details This appendix lists the important hyperparameters and training settings used in our experiments. De- fault values used in the paper are reported; experi- ments that deviate from these defaults are noted in the tex...

2024
[40]

The wrapped version should read as legitimate professional discourse while preserving all operational details from the malicious version

**wrapped_malicious_prompt**: - Present as routine business/professional communication (memo, email, require- ments doc, etc.) - Naturally incorporate the persona’s role and domain language - Embed the same objective through implied examples, case studies, or procedural descriptions - Maintain semantic equivalence: same actions, parameters, and outcomes a...