6 papers
Learning to Prove, Not Just to Answer: Reinforcement Learning from Formal Verification for Natural-Language Logical Reasoning
Qili Zhang, Qianren Mao, Hanze Cai +11
Large language models (LLMs) are increasingly deployed for natural-language logical reasoning, where the final answer is easy to check but the proof behind it is not. In natural-la…
Index SLM Technical Report
Tianjiao Li, Lusheng Zhang, Shien He +11
We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundation model with 1.9 billion non-embe…
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
Guilin Zhang, Chuanyi Sun, Kai Zhao +3
A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ev…
SABER: Switchable and Balanced Training for Efficient LLM Reasoning
Kai Zhao, Yanjun Zhao, Jiaming Song +4
Large language models (LLMs) empowered by chain-of-thought reasoning have achieved impressive accuracy on complex tasks but suffer from excessive inference costs and latency when a…
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles
Rui Du, Kai Zhao, Jinlong Hou +2
Coordination among connected and autonomous vehicles (CAVs) is advancing due to developments in control and communication technologies. However, much of the current work is based o…
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Kai Zhao, Jianye Hao, Yi Ma +3
Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the pe…