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Found 6 papers, 1 papers with code
Binary Classifier Optimization for Large Language Model Alignment
In the process of this discovery, we identified two techniques for effective alignment: reward shift and underlying distribution matching.
Hexa: Self-Improving for Knowledge-Grounded Dialogue System
A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e. g., web-search, memory retrieval) with modular approaches.
Effortless Integration of Memory Management into Open-Domain Conversation Systems
Open-domain conversation systems integrate multiple conversation skills into a single system through a modular approach.
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
In order to resolve these issues, in this paper, we propose a learnable hash-based episodic count, which we name LECO, that efficiently performs as a task-specific intrinsic reward in hard exploration problems.
GMAC: A Distributional Perspective on Actor-Critic Framework
In this paper, we devise a distributional framework on actor-critic as a solution to distributional instability, action type restriction, and conflation between samples and statistics.
A Distributional Perspective on Actor-Critic Framework
Recent distributional reinforcement learning methods, despite their successes, still contain fundamental problems that can lead to inaccurate representations of value distributions, such as distributional instability, action type restriction, and biased approximation.
