@ekzhu: I read the RLM paper and it’s like, this is the simplest way to solve a general problem, seriously it’s just this simple.
Summary
A researcher comments on the simplicity and elegance of the RLM paper, comparing it to the influential ReAct paper and expressing appreciation for its straightforward approach to solving general problems.
View Cached Full Text
Cached at: 04/20/26, 09:39 AM
I read the RLM paper and it’s like, this is the simplest way to solve a general problem, seriously it’s just this simple. Love this kind of vibe. Last one like this for me was the ReAct paper from 4 years ago, and that one defined the agents we use today. I made a visualization
Similar Articles
@jiqizhixin: Awesome blog! State of RL for reasoning LLMs https://aweers.de/blog/2026/rl-for-llms/…
A comprehensive blog post reviewing the state of reinforcement learning for reasoning LLMs, covering methods from REINFORCE and PPO to GRPO and beyond, with connections to key models like InstructGPT and DeepSeek-R1.
@ickma2311: CMU Advanced NLP: Reinforcement Learning I had been curious about how RL works on top of LLMs, and this CMU lecture mad…
CMU Advanced NLP lecture clarifies how reinforcement learning optimizes whole-output rewards (correctness, helpfulness, safety) rather than next-token prediction used in pretraining/fine-tuning.
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
This paper challenges the assumption that RL teaches new reasoning capabilities to LLMs, arguing instead that it performs sparse policy selection at high-entropy decision points. It introduces ReasonMaxxer, an RL-free method that matches full RL performance with significantly lower training costs.
@NFTCPS: Want to master Reinforcement Learning? Keep dreaming, bro. Online courses just teach you how to call APIs, leaving you utterly confused after finishing. Reading papers? Mountains of formulas will scare you off instantly. Trying to systematically understand the principles? The barrier to entry feels like climbing to heaven, and the learning path is as tangled as a maze. Recently, I stumbled upon an open-source book, 'Mathematical Foundations of Reinforcement Learning,' that pierces right through this fog. It provides a crystal-clear roadmap: starting from mathematics…
Introduces an open-source book, 'Mathematical Foundations of Reinforcement Learning,' which offers a rigorous yet accessible mathematical approach to RL, using grid world examples to clarify algorithmic logic.
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
TRN-R1-Zero introduces a post-training framework that enables LLMs to perform zero-shot reasoning on text-rich networks using only reinforcement learning, without supervised fine-tuning or chain-of-thought data.