2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Blockwise Advantage Estimation for Multi-Objective RL with Verifiable Rewards
Kirill Pavlenko, Alexander Golubev, Simon Karasik +1
Group Relative Policy Optimization (GRPO) assigns a single scalar advantage to all tokens in a completion. For structured generations with explicit segments and objectives, this co…
LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
Alexander Samarin, Sergei Krutikov, Anton Shevtsov +3
Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that are then verified in parall…
Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning
Alexander Golubev, Maria Trofimova, Sergei Polezhaev +9
Research on applications of reinforcement learning (RL) to large language models has mostly been focused on single-turn problems, such as mathematical reasoning or single-shot code…
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization
Maxim Kaledin, Alexander Golubev, Denis Belomestny
Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimat…