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cs.LG2026
Modification-Considering Value Learning for Reward Hacking Mitigation in RL
Evgenii Opryshko, Umangi Jain, Igor Gilitschenski
Reinforcement learning agents can exploit misspecified reward signals to achieve high apparent returns while failing on the intended objective, a failure mode known as reward hacki…
cs.LG2026
Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
Evgenii Opryshko, Junwei Quan, Claas Voelcker +2
Offline goal-conditioned reinforcement learning (GCRL) often struggles with long-horizon tasks, where errors in value estimation accumulate and produce unreliable policies. It is t…
cs.LG2026
Robust Reasoning Benchmark
Pavel Golikov, Evgenii Opryshko, Gennady Pekhimenko +1
While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their problem-solving abilities depend on the context and textual formatting. We in…