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Found 11 papers, 3 papers with code
On Speculative Decoding for Multimodal Large Language Models
We show that a language-only model can serve as a good draft model for speculative decoding with LLaVA 7B, bypassing the need for image tokens and their associated processing components from the draft model.
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs
In this paper, we propose a simple draft model training framework for direct alignment to chat-capable target models.
Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement
We empirically evaluate RSD with Llama 2 and OPT models, showing that RSD outperforms the baseline methods, consistently for fixed draft sequence length and in most cases for fixed computational budgets at LLM.
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces.
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
We consider offline imitation learning (IL), which aims to mimic the expert's behavior from its demonstration without further interaction with the environment.
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
We consider the offline reinforcement learning (RL) setting where the agent aims to optimize the policy solely from the data without further environment interactions.
Regularized Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator.
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic
Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies for our agents and reward functions that promote expert-like behavior.
A Bayesian Approach to Generative Adversarial Imitation Learning
Generative adversarial training for imitation learning has shown promising results on high-dimensional and continuous control tasks.
