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Lifting the Veil on Hyper-parameters for Value-based Deep Reinforcement Learning

Cohere

Successful applications of deep reinforcement learning (deep RL) combine algorithmic design and careful hyper-parameter selection.

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ResearchSoft-SVeRL: Self-Verified Reinforcement Learning with Soft RewardsCohere ResearchCountering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement LearningCohere ResearchPeriodic agent-state based Q-learning for POMDPsCohere ResearchPrioritized Training on Points that are Learnable, Worth Learning, and Not Yet LearntCohere ResearchRewardBench 2: Advancing Reward Model EvaluationCohere ResearchNo Need for Explanations: LLMs can implicitly learn from mistakes in-contextCohere BlogsDeconstructing Type Parameters - The Go Programming LanguageGo LearnFree "DuckDB in Action" BookMotherduck BlogsAn AI Chip With Unprecedented Performance To Do the Unimaginable - CerebrasCerebras BlogsBest Practices for Multi-Turn RLFireworks BlogsBeyond Supervised Fine Tuning: How Reinforcement Learning Empowers AI with Minimal LabelsFireworks ResourcesRANGE_GROUP_NOT_VALIDVercel ResourcesLimits and Pricing for Image OptimizationVercel LearnViews and App HomeVercel BlogsThe value of llms.txt: Hype or real?Mintlify BlogsRestrict access to your sites with role based redirectsNetlify BlogsBuilding a Deep Research Agent with Neon and Durable Endpoints - NeonNeon ResourcesParams structFastly ResourcesBuilder structFastly ResearchCIRCLE: A Framework for Evaluating AI from a Real-World LensCohere