RREDCoT approximates segment-level reward redistribution for CoT traces by querying the model itself, offering a lower-cost alternative to Monte Carlo credit assignment in reasoning-model RL.
Aligning dialogue agents with global feedback via large language model reward decomposition
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
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.
A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.
citing papers explorer
-
RREDCoT: Segment-Level Reward Redistribution for Reasoning Models
RREDCoT approximates segment-level reward redistribution for CoT traces by querying the model itself, offering a lower-cost alternative to Monte Carlo credit assignment in reasoning-model RL.
-
Trust Region On-Policy Distillation
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.