vOPD stabilizes on-policy distillation gradients by subtracting a closed-form per-token negative reverse KL baseline as a detached control variate, preserving unbiasedness while lowering variance and matching expensive full-vocabulary methods.
Part i: Tricks or traps? a deep dive into rl for llm reasoning
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 11roles
background 2polarities
background 2representative citing papers
Theoretical analysis of RLVR update dynamics leads to ACPO, an adaptive clipping method that outperforms DAPO and CISPO on reasoning benchmarks with 3B and 7B models.
PROPEL amortizes solver evaluation with a trained activation probe to optimize task generators toward a target solve rate, raising the share of learnable tasks from ~10% to ~20% in coding and SWE experiments.
CRPO applies counterfactual videos and a cross-branch relation reward in RL post-training to reduce shortcut reliance in Video LLMs, with gains shown on the new DyBench paired benchmark.
Balanced Aggregation fixes sign-length coupling and length downweighting in GRPO by computing separate token means for positive and negative subsets and combining them with sequence-count weights, yielding more stable training and higher benchmark scores.
MARL-Rad trains region-specific and global agents with reinforcement learning on clinical rewards to produce more accurate radiology reports than prior methods on MIMIC-CXR and IU X-ray datasets.
NPR trains LLMs to reason in parallel via self-distilled RL, delivering up to 24.5% performance gains and 4.6x speedups with 100% genuine parallel execution on reasoning benchmarks.
Survey mapping RL techniques onto LLM training and highlighting gaps in value-based, off-policy, and bootstrapping methods.
TrOPD stabilizes on-policy distillation for LLMs with trust-region learning, outlier estimation, and off-policy guidance, outperforming prior OPD methods on reasoning and code benchmarks.
DVAO dynamically weights multi-objective advantages by rollout-group reward variance to bound magnitudes, add cross-objective regularization, and outperform static baselines on math and tool-use tasks with Qwen models.
TeamPath introduces a reinforcement-learning-powered multimodal AI copilot for pathology that generates reasoned diagnoses and integrates image and transcriptomic data.
citing papers explorer
-
TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots
TeamPath introduces a reinforcement-learning-powered multimodal AI copilot for pathology that generates reasoned diagnoses and integrates image and transcriptomic data.