5 papers
Trust-Region Behavior Blending for On-Policy Distillation
Daniil Plyusov, Alexey Gorbatovski, Alexey Malakhov +4
On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mismatch of offline distillation,…
F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare
Daniil Plyusov, Alexey Gorbatovski, Boris Shaposhnikov +4
Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates. In practice, computational limits oft…
The Differences Between Direct Alignment Algorithms are a Blur
Alexey Gorbatovski, Boris Shaposhnikov, Viacheslav Sinii +2
Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in their use of SFT (one-stage vs. tw…
ESSA: Evolutionary Strategies for Scalable Alignment
Daria Korotyshova, Boris Shaposhnikov, Alexey Malakhov +7
Alignment of Large Language Models (LLMs) typically relies on Reinforcement Learning from Human Feedback (RLHF) with gradient-based optimizers such as Proximal Policy Optimization…
Learn Your Reference Model for Real Good Alignment
Alexey Gorbatovski, Boris Shaposhnikov, Alexey Malakhov +5
Despite the fact that offline methods for Large Language Models (LLMs) alignment do not require a direct reward model, they remain susceptible to overoptimization. This issue arise…