SalwaMK·May 28What If the Problem in AI Trading Isn’t the Model, But the Reward Function?When people talk about AI in trading, the conversation usually focuses on models:
ML Point·Apr 18LongAct: Training Models to Stay Stable When Context Gets LongThere is a point where language models stop failing apparently but failing due to long context.
Anvitha H·Mar 24Rethinking PPO: My Empirical Study of A*-PO for LLM Self-CorrectionThe Consistency Problem in LLM Reasoning Large Language Models (LLMs) have become surprisingly good at reasoning tasks, but their…
Dixon·Oct 20, 2025Information Gain-Based Policy Optimization: A Simple and Effective Approach for Multi-Turn LLM…How dense intrinsic rewards could revolutionize agentic reinforcement learning for large language models.
Nikhil Verma·Sep 2, 2025Modern Alignment and Post-training of LLMsLLMs, though powerful, often generate unhelpful or unpredictable outputs if left solely to their pretraining data. Post-training alignment…
Dr. Vijay Srinivas Agneeswaran·Jul 7, 2025Post on Self Improving LLMs Through Recursive Problem Decomposition (LADDER)Came across another interesting paper known as Self Improving LLMs Through Recursive Problem Decomposition (LADDER) is a methodology that…
Shivang Shrivastav·Mar 28, 2025REINFORCE Code Implementation: Mastering Policy Gradients for Reinforcement LearningBalancing the Cart-Pole: A Practical Implementation of the REINFORCE Algorithm
Shivang Shrivastav·Mar 28, 2025Proximal Policy Optimization (PPO): Clipping, Stability, and Sample Efficiency in Reinforcement…Beyond Vanilla Policy Gradients: PPO for Enhanced Performance in Complex Environments
Syed Hasan·Mar 24, 2025DAPO (Decoupled Clip and Dynamic sAmpling Policy Optimization): GRPO on steroids1. Introduction
InGenerative AIbyRakib.ai·Mar 19, 2025RL #6 — Reinforcement Learning ExplainedAdvanced Policy Optimization: The Evolution of Deep Reinforcement Learning
