#llm #training #dpo #vs #rlhf #ppo #reinforcement_learning #rl #gen_ai #NeurIPS
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://arxiv.org/abs/2305.18290v2
#deepmind #mistral #team #dpo #benchmarks #moe #llm #gen_ai
Mixtral of experts. A high quality Sparse Mixture-of-Experts.
https://mistral.ai/news/mixtral-of-experts
#offline_rl #rl
Revisiting the Minimalist Approach to Offline Reinforcement Learning
https://arxiv.org/abs/2305.09836
#agi #gen_ai #benchmarks
Levels of AGI: Operationalizing Progress on the Path to AGI
https://arxiv.org/abs/2311.02462v2
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://arxiv.org/abs/2305.18290v2
#deepmind #mistral #team #dpo #benchmarks #moe #llm #gen_ai
Mixtral of experts. A high quality Sparse Mixture-of-Experts.
https://mistral.ai/news/mixtral-of-experts
#offline_rl #rl
Revisiting the Minimalist Approach to Offline Reinforcement Learning
https://arxiv.org/abs/2305.09836
#agi #gen_ai #benchmarks
Levels of AGI: Operationalizing Progress on the Path to AGI
https://arxiv.org/abs/2311.02462v2
arXiv.org
Revisiting the Minimalist Approach to Offline Reinforcement Learning
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity. While these...
For Developers
https://youtu.be/-AB7b-XGaCU?si=I7sMBTvPb86JuSw6&t=496 #forecast #grok #tesla #team #open #llm #llama #vs #falcon #meta #team https://arxiv.org/abs/2311.16867
#team #berkeley #rag #haystack #haystack_ai
The Shift from Models to Compound AI Systems
https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/
claims:
- SOTA results are increasingly obtained by AI systems with multiple components instead of monolithic models
- Key trends in 2024 and beyond
The Shift from Models to Compound AI Systems
https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/
claims:
- SOTA results are increasingly obtained by AI systems with multiple components instead of monolithic models
- Key trends in 2024 and beyond