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cs.LG2026
KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization
Sihwan Park, Sung-Yub Kim, Doohyuk Jang +1
Asynchronous rollout improves the efficiency of reinforcement learning with verifiable rewards (RLVR) by reducing under-utilization during long-tail generation. Sustained concurren…
cs.LG2026
Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
Haechan Kim, Soohyun Ryu, Gyouk Chu +2
Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for improving the reasoning capabilities of large language models. However,…
cs.LG2025
Verifying Meta-Awareness via Predictive Rewards in Reasoning Models
Yoonjeon Kim, Doohyuk Jang, Eunho Yang
Recent research on reasoning models explores the meta-awareness of language models, including their ability to determine optimal thinking duration, recognize knowledge boundaries,…