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Found 8 papers, 0 papers with code
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains.
Process Supervision-Guided Policy Optimization for Code Generation
Reinforcement Learning (RL) with unit test feedback has enhanced large language models (LLMs) code generation, but relies on sparse rewards provided only after complete code evaluation, limiting learning efficiency and incremental improvements.
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
Reinforcement Learning (RL) plays a crucial role in aligning large language models (LLMs) with human preferences and improving their ability to perform complex tasks.
Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems
Finding optimal adversarial attack strategies is an important topic in reinforcement learning and the Markov decision process.
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties.
Efficient Action Poisoning Attacks on Linear Contextual Bandits
We show that, in both white-box and black-box settings, the proposed attack schemes can force the LinUCB agent to pull a target arm very frequently by spending only logarithm cost.
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning
In this paper, we introduce a new class of attacks named action poisoning attacks, where an adversary can change the action signal selected by the agent.
Action-Manipulation Attacks Against Stochastic Bandits: Attacks and Defense
To defend against this class of attacks, we introduce a novel algorithm that is robust to action-manipulation attacks when an upper bound for the total attack cost is given.
