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Source channel @githubtrending · Post #15340 · Dec 17

#python#gym#gym_environment#reinforcement_learning#reinforcement_learning_agent#reinforcement_learning_environments#rl_environment#rl_training NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior. https://github.com/NVIDIA-NeMo/Gym

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Crypto M - Crypto News

@CryptoM · Post #65294 · 04/12/2026, 03:15 PM

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🚀 TRON Network Implements Proposal 106 to Enhance Compatibility TRON Network has officially passed Proposal 106 as of April 10 at 20:00 UTC+8. According to ChainCatcher, the mainnet has adjusted the behavior and execution cost of the SELFDESTRUCT instruction. Contracts will only be permanently removed from the blockchain if the instruction is called within the same transaction as the contract's creation. Otherwise, executing the instruction will merely transfer the assets within the contract to a specified address, without deleting the contract itself. Additionally, the energy consumption for SELFDESTRUCT has been changed from 0 to 5000. This adjustment aligns TRON's mechanisms more closely with Ethereum, enhancing the compatibility of the TRON Virtual Machine (TVM). It aims to provide more stable foundational support for multi-chain development and ecosystem adaptation. #TRON#Proposal106#Blockchain#Ethereum#TVM#SELFDESTRUCT#Mainnet#MultiChain#Ecosystem#Crypto#ETH#TRX

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