Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning | Cohere | Research | TechiSeek

AddAdd your Listings

Research

Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning

While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO).

Research Cohere

ResearchSoft-SVeRL: Self-Verified Reinforcement Learning with Soft RewardsCohere ResearchImproving Policy Learning via Language Dynamics DistillationCohere ResearchRLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMsCohere ResearchWhen Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference LearningCohere ResearchRewardBench 2: Advancing Reward Model EvaluationCohere ResearchLLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable ObjectivesCohere NewsMachine Learning Algorithm Sidesteps the Scientific MethodThenewstack BlogsGCP Pub/Sub connector for ClickPipes is now in Private PreviewClickhouse BlogsJuly Tailscale newsletterTailscale BlogsFirst Block with Adeyemi Ajao, Co-founder and Managing Partner at Base10Notion So BlogsLogbook: October 21 to 28, 2022Fly BlogsAccelerating Code Completion with Fireworks Fast LLM InferenceFireworks BlogsFrom data residency to digital sovereignty: Architectural patterns for cloud native platformsCncf BlogsImproving Composer through real-time RL · CursorCursor BlogsSpeeding up GPU kernels by 38% with a multi-agent system · CursorCursor ResourcesAdvanced BotID ConfigurationVercel BlogsAugust 22nd DDoS Learning ReviewNetlify BlogsThe Jamstack Explorers Learning Platform: A Delirious Podcast RetroNetlify Products & ServicesNew this month: Performance upgrades, better LLM support, new blocks and moreGitbook BlogsIntent Prototyping: The Allure And Danger Of Pure Vibe Coding In Enterprise UX (Part 1)Smashingmagazine