34 verified PDFs
On six challenging math reasoning benchmarks, RL-PLUS achieves state-of-the-art (SOTA) performance, outperform- ing existing RLVR methods and improving upon SFT ...
Reinforcement learning (RL) is increasingly grounded in tools from proba- bility, optimization, and operator theory.
We provide a mathematical proof of the convergence of the approximation scheme, and we analyze rigorously the approximation by controlling the different sources ...
Principles for Designing Intervention in Mathematics •Explicit, Systematic Instruction •Effective Questioning •Concrete, Representational/Visual/Pictorial, ...
This paper develops a data-driven method for control of partial differential equations (PDE) based on deep reinforcement learning (RL) techniques. ...
This update equation gives rise to perhaps the simplest reinforcement learning algorithm, known as TD learning. In this equation, 𝛼 is a ...
by G Lederman · Cited by 48 — We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
We introduce a theorem proving algorithm that uses practically no domain heuris- tics for guiding its connection-style proof search.
Reinforcement learning (RL) is a general approach to solving reward-based problems. RL tries to mimic the way that human learns new things not from a ...
For each t ∈ N, let At = A(zt) and Bt = B(zt). From the stochastic gradient method in equation (12), we derive the equation (1) for our online.
We provide a concise introduction to basic approaches to reinforcement learning from the machine learning perspective. The focus is on value function and ...
We describe a new approach for understanding the difficulty of designing efficient learning algorithms. learnable in polynomial-time
The objective of reinforcement learning is to find the policy π. Bellman equation by a sampling variant Jπ(x) = Jπ(x) + α(r + γJπ(x 0 ) − Jπ(x)). Jπ(x) = R(x, ...
The method we propose consist on the iteration of an algorithm made of 3 modules : expansion, se- lection, compression. Expansion step consist on combining each.
