works on

From the 1 of 30 linked papers with an AI index.

activity
20242026
collaborators

30 papers

cs.LG2026

Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning

Yifu Huo, Shunjie Xing, Chenglong Wang +8

Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aim…

cs.LG2026

RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction

Chenglong Wang, Ziming Zhu, Yifu Huo +9

Recent advances in reward modeling show a paradigm shift from discriminative reward models to generative reward models. However, despite their strong capabilities in response ranki…

cs.LG2026

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

Kaiyang Ye, Yuan Ge, Junxiang Zhang +8

While on-policy distillation (OPD) effectively addresses sparse rewards and exposure bias in large language model post-training, its extension to flow models remains underexplored.…

cs.CL2026

ToFu: A White-Box, Token-Efficient Agent Harness for Researchers

Junhao Ruan, Yuan Ge, Bei Li +7

ToFu is an open‑source, white‑box agentic harness that lets researchers automate codebase reading, file editing, command execution, and tool integration with high token efficiency…

cs.CL2026

HybridThinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps

Xin Liu, Runsong Zhao, Xinyu Liu +8

Extended chain-of-thought (CoT) traces improve LLM reasoning but incur substantial computational and memory costs. While existing CoT compression methods mitigate this by condensin…

cs.LG2026

Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence

Xinyu Liu, Kechen Jiao, Chunyang Xiao +10

On-policy distillation (OPD) has become a promising paradigm for reasoning-oriented post-training of large language models (LLMs), especially when combined with reinforcement learn…