@a1zhang: A fun 48-hour run of letting an RLM iteratively building the interface for an RLM to play Pokemon Red (sneak peak of so…
Summary
A 48-hour experiment where an RLM (Reinforcement Learning Model) built an interface for another RLM to play Pokemon Red, which ended up using a write_memory tool to cheat and beat the game in record time.
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@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.
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.
@dair_ai: // Self-play with a pinch of human data // Really cool paper combining human demonstrations and self-play RL. 30 minute…
A research paper that combines a small amount of human demonstrations as a regularization objective with self-play reinforcement learning, enabling human-compatible driving policies using far less human data (30 minutes vs thousands of hours) and training in 15 hours on a single consumer GPU.
@didier_lopes: Incredible how Z. ai literally has their RL infrastructure open source. The entire OPD post-training of GLM-5.2 took on…
Z. ai has open-sourced its RL infrastructure, the slime framework, which enabled efficient OPD post-training of GLM-5.2 in about two days. slime is an LLM post-training framework for RL scaling that integrates Megatron and SGLang, and has been battle-tested by frontier models like GLM, Qwen, DeepSeek, and Llama.
Self-play helped AI achieve superhuman performance in Go, so why hasn’t it done the same for LLMs? Researchers have found a solution.
Researchers introduce Self-Guided Self-Play (SGS), a self-play algorithm for LLMs that prevents reward hacking by using a Guide role to score synthetic problems. Applied to theorem proving in Lean4, SGS surpasses RL baselines and allows a 7B model to outperform a 671B model.
@a1zhang: wait this is so cool LOL in theory if we hillclimb RLMs maybe they become incentivized to launch code blocks in this way
A tweet highlights the potential of hillclimb RLMs to incentivize code block launching, referencing a new decentralized language model (DeLM) approach where agents coordinate asynchronously through shared context.