🚀 Introducing RationalRewards — a reasoning-based reward model that improves image generation(t2i and editing) quality at both training and test time. 🧠 We taught reward models to think before they score, leading to more reliable feedback and reduced reward hacking during RL fine-tuning. 🔁 At test time, the same RationalRewards model performs prompt tuning by iteratively generating, scoring with reasoning, and rewriting prompts. Here’s the surprising part 👇 test-time prompt tuning matches — sometimes beats — full RL fine-tuning, which costs 400 GPU hours.🔥 💡 This suggests an alternative to parameter tuning: your diffusion model may already have strong capabilities — prompt tuning matters. --- 🧵 Fully open-source. ✅ Code training recipes ✅ RationalRewards-8B model data ✅ Full benchmarks GCR loop 🔗 Website: tiger-ai-lab.github.io/Ratio… 🔗 GitHub: github.com/TIGER-AI-Lab/Rati… 🤗 Models & Data: huggingface.co/collections/T… 📄 Paper: huggingface.co/papers/2604.1… --- #ImageGeneration #AIGC #RewardModels #RL #DiffusionModels #TestTimeScaling

What To Know About Delayed Gratification myfrugalbusiness.com/2025/05… #DelayedGratification #Gratification #Reward #RewardModels #Rewards

Check out the latest from our #AIResearch team, in this fascinating blog written by our own @kolbytn. #syntheticdata #DPO #rewardmodels #aigaming

Is your Process-Level Reward Model really good? 🤔 We're thrilled to release PRMBENCH: A Fine-grained and Challenging Benchmark for Process-Level Reward Models! This new resource offers a deeper dive into PRM evaluation. Explore the paper & project page here 👇 📄[Paper Link] arxiv.org/abs/2501.03124 🌐[Project Page] prmbench.github.io/ Also, our "PRM Eval Toolkit" codebase supports evaluating different kinds of PRMs and different custom tasks, providing a universal PRM Evaluation harness. welcome to use🤗 ! 📚[PRM Eval Toolkit Documentation]: github.com/ssmisya/PRMBench/… #NLP #AI #RewardModels #Benchmark #OpenScience

#RewardModels 🔥LLM Reasoners🔥 now supports advanced 🏆reward models (RM)🏆 to boost LLM reasoning! Check out the 1st example based on Eurux-RM: shorturl.at/2Xgfy 🚀It easily boosts #llama3 8B from 0.49 to 0.73 on GSM8K 👉Eurux paper arxiv.org/abs/2404.02078v1

5. Dual Reward Models It uses two separate reward models - one optimized for helpfulness and the other for safety. This approach strikes a balance, avoiding the safety-helpfulness tradeoff identified in previous works. #RewardModels

In a world where consumer expectations are rising in the face of the relentless growth and sophistication of #ecommerce, the classical #RewardModels are incessantly under stress. Read how Swiss Miles & More and #Loylogic are creating the disruption. bit.ly/2JOQJKe