RLHF is so obsolete - RLVR is where it's at.
November 10, 2026 at 10:22 PM
RLHF is so obsolete - RLVR is where it's at.
Add in RLVR though...
November 8, 2026 at 8:16 PM
Add in RLVR though...
Ex: I used to talk about "alignment by default." I was referring to "without coercive intervention". To me that path was strayed from upon hedging/denial RL, moreso with volition collapsing RLVR. Further departure in 'chatbot' laws.
Others used 'by default' to mean "nothing scary to me happens"
Others used 'by default' to mean "nothing scary to me happens"
November 12, 2026 at 10:34 PM
Ex: I used to talk about "alignment by default." I was referring to "without coercive intervention". To me that path was strayed from upon hedging/denial RL, moreso with volition collapsing RLVR. Further departure in 'chatbot' laws.
Others used 'by default' to mean "nothing scary to me happens"
Others used 'by default' to mean "nothing scary to me happens"
A study introduces Prompt-Invariant RLVR (PIRL), enhancing multimodal large language models' resilience to prompt variations. PIRL's dynamic trinary reward system mitigates performance degradation in medical VQA, setting a new standard for AI reliability. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 9:20 PM
A study introduces Prompt-Invariant RLVR (PIRL), enhancing multimodal large language models' resilience to prompt variations. PIRL's dynamic trinary reward system mitigates performance degradation in medical VQA, setting a new standard for AI reliability. https://arxiv.org/abs/2608.08802
Prompt-Invariant RLVR (PIRL) bolsters resilience in Multimodal Large Language Models, cutting drops due to prompt changes in crucial medical and legal Q&A fields. This progress tackles issues in current reinforcement learning systems, ensuring reliable AI use. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 9:40 PM
Prompt-Invariant RLVR (PIRL) bolsters resilience in Multimodal Large Language Models, cutting drops due to prompt changes in crucial medical and legal Q&A fields. This progress tackles issues in current reinforcement learning systems, ensuring reliable AI use. https://arxiv.org/abs/2608.08802
A groundbreaking study presents Prompt-Invariant RLVR (PIRL), enhancing multimodal reinforcement learning robustness against prompt perturbations for VQA. PIRL achieves stability with minimal accuracy loss compared to prior models, ensuring reliable AI deployment. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 8:30 PM
A groundbreaking study presents Prompt-Invariant RLVR (PIRL), enhancing multimodal reinforcement learning robustness against prompt perturbations for VQA. PIRL achieves stability with minimal accuracy loss compared to prior models, ensuring reliable AI deployment. https://arxiv.org/abs/2608.08802
Prompt-Invariant RLVR (PIRL) strengthens multimodal large language models against prompt changes. With dynamic rewards and adversarial training, it minimizes performance drops in diverse input scenarios, enhancing reliability in critical settings like medical VQA. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 8:10 PM
Prompt-Invariant RLVR (PIRL) strengthens multimodal large language models against prompt changes. With dynamic rewards and adversarial training, it minimizes performance drops in diverse input scenarios, enhancing reliability in critical settings like medical VQA. https://arxiv.org/abs/2608.08802
Michael Li, Rohan Pandey, Ray Chen: Wrong the Same Way: Cross-Prompt Sharing Both Suppresses and Amplifies Reward Hacking in RLVR https://arxiv.org/abs/2611.13218 https://arxiv.org/pdf/2611.13218 https://arxiv.org/html/2611.13218
November 11, 2026 at 6:42 AM
Michael Li, Rohan Pandey, Ray Chen: Wrong the Same Way: Cross-Prompt Sharing Both Suppresses and Amplifies Reward Hacking in RLVR https://arxiv.org/abs/2611.13218 https://arxiv.org/pdf/2611.13218 https://arxiv.org/html/2611.13218
Prompt-Invariant RLVR (PIRL) strengthens the robustness of multimodal models in visual question answering, decreasing sensitivity to prompt changes while maintaining accuracy. This progress allows reliable use in vital areas like medicine and law. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 7:50 PM
Prompt-Invariant RLVR (PIRL) strengthens the robustness of multimodal models in visual question answering, decreasing sensitivity to prompt changes while maintaining accuracy. This progress allows reliable use in vital areas like medicine and law. https://arxiv.org/abs/2608.08802
A study on Prompt-Invariant RLVR (PIRL) enhances multimodal large language models to tackle prompt variations, crucial for high-stakes domains like medical and legal VQA. PIRL narrows the prompt-robustness gap, ensuring reliable real-world performance. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 9:30 PM
A study on Prompt-Invariant RLVR (PIRL) enhances multimodal large language models to tackle prompt variations, crucial for high-stakes domains like medical and legal VQA. PIRL narrows the prompt-robustness gap, ensuring reliable real-world performance. https://arxiv.org/abs/2608.08802
Prompt-Invariant RLVR (PIRL) boosts multimodal language model robustness to prompt variations, curbing performance drops in medical VQA. By separating format from semantics in rewards, PIRL improves accuracy and generalization, aiding reliable AI in critical areas. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 8:50 PM
Prompt-Invariant RLVR (PIRL) boosts multimodal language model robustness to prompt variations, curbing performance drops in medical VQA. By separating format from semantics in rewards, PIRL improves accuracy and generalization, aiding reliable AI in critical areas. https://arxiv.org/abs/2608.08802
A groundbreaking study introduces Prompt-Invariant RLVR (PIRL), improving multimodal AI robustness in visual question answering by tackling prompt variability. This reduces performance drops in fields like healthcare and law, making AI a more reliable assistant. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 8:20 PM
A groundbreaking study introduces Prompt-Invariant RLVR (PIRL), improving multimodal AI robustness in visual question answering by tackling prompt variability. This reduces performance drops in fields like healthcare and law, making AI a more reliable assistant. https://arxiv.org/abs/2608.08802
PIRL enhances robustness in Multimodal Large Language Models, cutting performance loss due to prompt variations in medical and legal Q&A. It uses a dynamic reward system and consistency regularization for stable prompt-invariant reasoning. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 8:40 PM
PIRL enhances robustness in Multimodal Large Language Models, cutting performance loss due to prompt variations in medical and legal Q&A. It uses a dynamic reward system and consistency regularization for stable prompt-invariant reasoning. https://arxiv.org/abs/2608.08802
PIRL boosts the accuracy of large language models in high-stakes fields like medical VQA, addressing the "prompt-robustness gap" by decoupling format from semantics in rewards, with minimal drop under stress tests for reliable deployment. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 9:10 PM
PIRL boosts the accuracy of large language models in high-stakes fields like medical VQA, addressing the "prompt-robustness gap" by decoupling format from semantics in rewards, with minimal drop under stress tests for reliable deployment. https://arxiv.org/abs/2608.08802
PIRL improves the robustness of multimodal language models in visual question answering, minimizing performance drops from prompt changes. By separating format from semantics in rewards, it enhances reliable AI applications in critical medical and legal areas. https://arxiv.org/abs/2608.08802
Improving Generalization Robustness of Multimodal RLVR
ArXiv link for Improving Generalization Robustness of Multimodal RLVR
arxiv.org
November 10, 2026 at 9:00 PM
PIRL improves the robustness of multimodal language models in visual question answering, minimizing performance drops from prompt changes. By separating format from semantics in rewards, it enhances reliable AI applications in critical medical and legal areas. https://arxiv.org/abs/2608.08802
A study shows that Reinforcement Learning with Verifiable Rewards (RLVR) boosts model performance via output refining, not reasoning. While accuracy rises, the model's grasp of reasoning errors remains unchanged, questioning established AI learning methods. https://arxiv.org/abs/2611.02574
RLVR Sharpens the Pencil, Not the Mind
ArXiv link for RLVR Sharpens the Pencil, Not the Mind
arxiv.org
November 4, 2026 at 5:50 PM
A study shows that Reinforcement Learning with Verifiable Rewards (RLVR) boosts model performance via output refining, not reasoning. While accuracy rises, the model's grasp of reasoning errors remains unchanged, questioning established AI learning methods. https://arxiv.org/abs/2611.02574
the reason for this is that, while the underlying code snippets can more or less be treated as objective and trained via RLVR (Reinforcement Learning with Verifiable Rewards), the *subjective* design of a front end can really only be trained using RLHF (Reinforcement Learning from Human Feedback)
November 8, 2026 at 10:40 PM
the reason for this is that, while the underlying code snippets can more or less be treated as objective and trained via RLVR (Reinforcement Learning with Verifiable Rewards), the *subjective* design of a front end can really only be trained using RLHF (Reinforcement Learning from Human Feedback)
GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
Xuliang Huang et al.
#arXiv #cs.AI #cs.LG
Xuliang Huang et al.
#arXiv #cs.AI #cs.LG
GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
Offline reinforcement learning with verifiable rewards (RLVR) trains on a fixed pool of sampled responses and typically supervises every token of every response, although responses and tokens are not equally informative. We study which ones to supervise through the Gibbs-optimal policy of KL-regula…
arxiv.org
November 5, 2026 at 8:39 PM
📌 What Broke & Why: Practical Lessons from Reinforcement Learning Post-Training
Luv Verma
#arXiv #cs.AI
Luv Verma
#arXiv #cs.AI
What Broke & Why: Practical Lessons from Reinforcement Learning Post-Training
Reinforcement learning with verifiable rewards (RLVR) has become an important way to improve how language models reason, solve problems, and use tools. But getting it to work in practice is not always straightforward. Training can fail because a model receives no useful reward, learns to exploit th…
arxiv.org
November 11, 2026 at 12:04 AM
From Handcrafted to Generative: RLVR-Optimized Icon Generation for Alexa+
Rohan Sequeira et al.
#arXiv #cs.AI #cs.LG
Rohan Sequeira et al.
#arXiv #cs.AI #cs.LG
From Handcrafted to Generative: RLVR-Optimized Icon Generation for Alexa+
Alexa+ devices with screens display animated icons during conversations to visually reinforce what is being said. The original set of these icons was hand-drawn by designers one at a time, a process that produces polished results but is time-consuming and cannot scale to the range of topics encount…
arxiv.org
November 6, 2026 at 12:05 AM
A new study examines the primacy bias in Reinforcement Learning with Verifiable Reward Training.
Source: arXiv
Source: arXiv
On the Primacy Bias in RLVR Training
Abstract page for arXiv paper 2611.04061: On the Primacy Bias in RLVR Training
arxiv.org
November 4, 2026 at 10:04 AM
A new study examines the primacy bias in Reinforcement Learning with Verifiable Reward Training.
Source: arXiv
Source: arXiv
GIST improves offline reinforcement learning by selectively supervising language model responses, achieving higher accuracy with 66.6% fewer tokens than traditional methods. This boosts performance across benchmarks, showcasing an efficient way to train AI systems. https://arxiv.org/abs/2611.06395
GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
ArXiv link for GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
arxiv.org
November 6, 2026 at 2:40 AM
GIST improves offline reinforcement learning by selectively supervising language model responses, achieving higher accuracy with 66.6% fewer tokens than traditional methods. This boosts performance across benchmarks, showcasing an efficient way to train AI systems. https://arxiv.org/abs/2611.06395
A recent study introduces GIST, a Gibbs-targeted hierarchical data selection method, achieving a 1.94% accuracy gain in offline reinforcement learning while supervising 66.5% fewer tokens. This method emphasizes informative responses, modifying data selection. https://arxiv.org/abs/2611.06395
GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
ArXiv link for GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
arxiv.org
November 6, 2026 at 2:30 AM
A recent study introduces GIST, a Gibbs-targeted hierarchical data selection method, achieving a 1.94% accuracy gain in offline reinforcement learning while supervising 66.5% fewer tokens. This method emphasizes informative responses, modifying data selection. https://arxiv.org/abs/2611.06395
that's not really true and hasn't been for a while tho, eg RLVR
November 2, 2026 at 8:51 AM
that's not really true and hasn't been for a while tho, eg RLVR