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Collections including paper arxiv:2402.12479
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In deep reinforcement learning, a pruned network is a good network
Paper • 2402.12479 • Published • 17 -
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Paper • 2403.03950 • Published • 13 -
RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 67 -
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
Paper • 2405.11143 • Published • 33
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Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Paper • 2402.06102 • Published • 4 -
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Paper • 2402.08609 • Published • 34 -
In deep reinforcement learning, a pruned network is a good network
Paper • 2402.12479 • Published • 17 -
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Paper • 2402.14083 • Published • 47
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Diffusion World Model
Paper • 2402.03570 • Published • 7 -
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
Paper • 2401.16335 • Published • 1 -
Towards Efficient and Exact Optimization of Language Model Alignment
Paper • 2402.00856 • Published -
ODIN: Disentangled Reward Mitigates Hacking in RLHF
Paper • 2402.07319 • Published • 13
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Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Paper • 2310.17157 • Published • 11 -
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Paper • 2305.15805 • Published • 1 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Paper • 2110.07560 • Published • 1
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Moral Foundations of Large Language Models
Paper • 2310.15337 • Published • 1 -
Specific versus General Principles for Constitutional AI
Paper • 2310.13798 • Published • 2 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47