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Found 17 papers, 8 papers with code
The Lessons of Developing Process Reward Models in Mathematical Reasoning
Furthermore, we identify potential biases in conventional Best-of-N (BoN) evaluation strategies for PRMs: (1) The unreliable policy models generate responses with correct answers but flawed processes, leading to a misalignment between the evaluation criteria of BoN and the PRM objectives of process verification.
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #7 on
on GPQA
ProcessBench: Identifying Process Errors in Mathematical Reasoning
We conduct extensive evaluation on ProcessBench, involving two types of models: process reward models (PRMs) and critic models, where for the latter we prompt general language models to critique each solution step by step.
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
This RM is then applied to the iterative evolution of data in supervised fine-tuning (SFT).
Ranked #2 on
Math Word Problem Solving
on MATH
(using extra training data)
Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence
Interactive artificial intelligence in the motion control field is an interesting topic, especially when universal knowledge is adaptive to multiple tasks and universal environments.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback
In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models.
Online Merging Optimizers for Boosting Rewards and Mitigating Tax in Alignment
Effectively aligning Large Language Models (LLMs) with human-centric values while preventing the degradation of abilities acquired through Pre-training and Supervised Fine-tuning (SFT) poses a central challenge in Reinforcement Learning from Human Feedback (RLHF).
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness.
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Based on this observation, we propose a data selector based on InsTag to select 6K diverse and complex samples from open-source datasets and fine-tune models on InsTag-selected data.
Large Sequence Models for Sequential Decision-Making: A Survey
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.
Contextual Transformer for Offline Meta Reinforcement Learning
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence.
Fully Decentralized Model-based Policy Optimization with Networked Agents
Model-based RL is an effective approach for reducing sample complexity.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
