We have 7 of 75 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Modern LLM post-training composes supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and on-policy distillation (OPD) into multi-stage pipelines, yet these stages are typically designed and evaluated in isolation. We show that this composition is consequential: a stage that improves th...
Just-in-Time Memory (JitMem) consistently outperforms no-memory agents as well as heuristic and learned write-time memory methods, improving over the strongest baseline by 16.2, 16.3, and 3.9 absolute success-rate points, respectively.
EvoHarnessBench is introduced, a benchmark for evaluating agents under controlled harness evolution across three axes (tools, skills, and agents), and establishes harness evolution as a distinct challenge for building agents that can keep pace with an evolving harness while preserving previously effective behavior.
Experiments spanning mathematical reasoning, multi-domain STEM, code generation, and multi-turn agentic tasks show that RISE outperforms RLVR-only training and on-policy self-distillation across all settings.
It is found that process-level verification does not consistently improve performance and frequently exhibits high variance, highlighting the difficulty of reliably evaluating partial multi-agent trajectories.
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
Procedural Memory Distillation is proposed, which converts crossepisode signals into reusable procedural memory and distills it into the policy's weights during training, yielding a memory-free model at inference.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
