Stack from ghstack (oldest at bottom): -> [BugFix] adapt log-prob TD batch-size to advantage shape in PPO #2756
① 强大的基座模型以及适当的提示遵循还是起到了开启整个强化旅程中非常关键的作用,正如之前我的一个假设:如果基础模型对于碎片化通用泛化能力掌握的足够完备,对于深推理这种场景也许会在RL优雅而稳健的探索及仅简单奖励下起到加速长链分布拼接(CoT泛化对齐)的效果,同时还有类<Think>指令遵循在打辅助。② 关于基于GAE的...
RL policy gradient 之 A2C, A3C,PPO小总结 A2C, A3C, PPO 都不是纯 policy based 的 RL 方法,准确地说是 Actor-Critic 方法,即,同时用到了 value function 和 policy funtion. 这三种方法之间有什么区别呢? A2C 这里的数字 2 其实是说有多少个 “A” 的意思, 作为 Actor-Critic 方法的一种,A2C 是...
We compare against both online RL (PPO) and recent preference-based (DPO, PRO) and reward-based (GOLD) offline RL baselines. On the commonly-used RLHF benchmark, Helpful and Harmless Assistant (HHA), LMs trained with A-LoL methods achieve the highest diversity while also being rated more ...
RL policy gradient 之 A2C, A3C,PPO小总结 A2C,A3C, PPO 都不是纯policybased 的RL方法,准确地说是Actor-Critic方法,即,同时用到了 value function 和policy...Actor-Critic方法的基础上多了一个advantage: r+v(s′)−v(s)r + v(s') - v(s)r+v(s′)−v(s)A3C很好理解 ...
Please use hyper parameters from this readme. With other hyper parameters things might not work (it's RL after all)! This is a PyTorch implementation of Advantage Actor Critic (A2C), a synchronous deterministic version ofA3C Proximal Policy OptimizationPPO ...
The role of reinforcement learning (RL) in enhancing the reasoning of large language models (LLMs) is becoming increasingly significant. Despite the success of RL in many scenarios, there are still many challenges in improving the reasoning of LLMs. One challenge is the sparse reward, which mak...
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pytorch-a2c-ppo-acktr Please use hyper parameters from this readme. With other hyper parameters things might not work (it's RL after all)! This is a PyTorch implementation of Advantage Actor Critic (A2C), a synchronous deterministic version ofA3C ...