1 citations · 1 across the 17 of their papers we have counts for
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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…
Learning Beyond What You Sample: Off-Policy-Aware Cross-Model Trajectory Exchange for RLVR
Doohyuk Jang, Yoonsik Park, Gyouk Chu +2
Reinforcement Learning with Verifiable Rewards (RLVR) methods such as GRPO rely on successful self-generated trajectories, but finite rollout budgets can produce all-fail groups wi…
J-Zero: Unified Challenger--Solver--Judge Self-Evolution from Zero Data
Gyouk Chu, Myeongho Jeon, Eunho Yang +1
Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human supervision. While considerable p…
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,…
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,…