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"reinforcement learning"20 resultsKeyword

Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data

Beginner

Emre Can Acikgoz, Cheng Qian et al.Feb 24arXiv

Tool-R0 teaches a language model to use software tools (like APIs) with zero human-made training data.

#self-play reinforcement learning#tool calling#function calling

CharacterFlywheel: Scaling Iterative Improvement of Engaging and Steerable LLMs in Production

Intermediate

Yixin Nie, Lin Guan et al.Mar 2arXiv

CharacterFlywheel is a step‑by‑step loop that steadily improves chatty AI characters by learning from real conversations on Instagram, WhatsApp, and Messenger.

#CharacterFlywheel#large language models#conversational AI

V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval

Intermediate

Dongyang Chen, Chaoyang Wang et al.Feb 5arXiv

V-Retrver is a new way for AI to search across text and images by double-checking tiny visual details instead of only guessing from words.

#V-Retrver#multimodal retrieval#agentic reasoning

Reinforced Attention Learning

Intermediate

Bangzheng Li, Jianmo Ni et al.Feb 4arXiv

This paper teaches AI to pay attention better by training its focus, not just its words.

#Reinforced Attention Learning#attention policy#multimodal LLM

Steering LLMs via Scalable Interactive Oversight

Intermediate

Enyu Zhou, Zhiheng Xi et al.Feb 4arXiv

The paper tackles a common problem: people can ask AI to do big, complex tasks, but they can’t always explain exactly what they want or check the results well.

#scalable oversight#interactive alignment#requirement elicitation

Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR

Intermediate

Fanfan Liu, Youyang Yin et al.Feb 5arXiv

The paper discovers that popular RLVR methods for training language and vision-language models secretly prefer certain answer lengths, which can hurt learning.

#LUSPO#RLVR#GRPO

BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation

Intermediate

Jingwen Xu, Yiyang Lu et al.Jan 30arXiv

BatCoder teaches a code model to write both code and its documentation by doing a round trip: from code to docs and back to code.

#back-translation#self-supervised learning#reinforcement learning for code

D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use

Intermediate

Bowen Xu, Shaoyu Wu et al.Feb 2arXiv

This paper fixes a common problem in reasoning AIs called Lazy Reasoning, where the model rambles instead of making a good plan.

#task decomposition#tool use#large reasoning models

CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs

Intermediate

Zhiyuan Yao, Yi-Kai Zhang et al.Feb 3arXiv

Large language models learn better when we spend more practice time on the right questions at the right moments.

#Reinforcement Learning#RLVR#GRPO

Self-Hinting Language Models Enhance Reinforcement Learning

Intermediate

Baohao Liao, Hanze Dong et al.Feb 3arXiv

When rewards are rare, a popular training method for language models (GRPO) often stops learning because every try in a group gets the same score, so there is nothing to compare.

#reinforcement learning#GRPO#self-hinting

ERNIE 5.0 Technical Report

Intermediate

Haifeng Wang, Hua Wu et al.Feb 4arXiv

ERNIE 5.0 is a single giant model that can read and create text, images, video, and audio by predicting the next pieces step by step, like writing a story one line at a time.

#ERNIE 5.0#unified autoregressive model#mixture-of-experts

Rethinking the Trust Region in LLM Reinforcement Learning

Intermediate

Penghui Qi, Xiangxin Zhou et al.Feb 4arXiv

The paper shows that the popular PPO method for training language models is unfair to rare words and too gentle with very common words, which makes learning slow and unstable.

#Reinforcement Learning#Proximal Policy Optimization#Trust Region

PromptRL: Prompt Matters in RL for Flow-Based Image Generation

Intermediate

Fu-Yun Wang, Han Zhang et al.Feb 1arXiv

PromptRL teaches a language model to rewrite prompts while a flow-based image model learns to draw, and both are trained together using the same rewards.

#PromptRL#flow matching#reinforcement learning

Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability

Intermediate

Xiao Liang, Zhong-Zhi Li et al.Feb 2arXiv

The paper trains language models to solve hard problems by first breaking them into smaller parts and then solving those parts, instead of only thinking in one long chain.

#divide-and-conquer reasoning#chain-of-thought#reinforcement learning

TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios

Intermediate

Yuanzhe Shen, Zisu Huang et al.Feb 2arXiv

TRIP-Bench is a new test that checks if AI travel agents can plan real trips over many chat turns while following strict rules and changing user requests.

#TRIP-Bench#long-horizon agents#multi-turn interaction

Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training

Intermediate

Ran Xu, Tianci Liu et al.Feb 2arXiv

The paper introduces Rubric-ARM, a system that teaches two AI helpers—a rubric maker and a judge—to learn together using reinforcement learning so they can better decide which answers people would prefer.

#Rubric-based reward modeling#LLM-as-a-judge#Alternating reinforcement learning

Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memory

Beginner

Zhenting Wang, Huancheng Chen et al.Mar 4arXiv

This paper teaches long-horizon AI agents to remember everything exactly without stuffing their whole memory at once.

#indexed memory#LLM agents#long-horizon tasks

Behavior Knowledge Merge in Reinforced Agentic Models

Intermediate

Xiangchi Yuan, Dachuan Shi et al.Jan 20arXiv

The paper solves a big problem: when you merge several reinforcement-learned models, their special skills get watered down by simple averaging.

#reinforcement learning#model merging#task vectors

Toward Cognitive Supersensing in Multimodal Large Language Model

Intermediate

Boyi Li, Yifan Shen et al.Feb 2arXiv

This paper teaches multimodal AI models to not just read pictures but to also imagine and think with pictures inside their heads.

#multimodal large language model#visual cognition#latent visual imagery

FARE: Fast-Slow Agentic Robotic Exploration

Beginner

Shuhao Liao, Xuxin Lv et al.Jan 21arXiv

Robots used to explore by following simple rules or short-term rewards, which often made them waste time and backtrack a lot.

#autonomous exploration#fast-slow thinking#hierarchical planning