SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 2501–2510 of 15113 papers

TitleStatusHype
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains—0
SATURN: SAT-based Reinforcement Learning to Unleash Language Model ReasoningCode0
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine TranslationCode0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
Dynamic Sampling that Adapts: Iterative DPO for Self-Aware Mathematical Reasoning—0
Reinforcement Learning for Stock Transactions—0
LARES: Latent Reasoning for Sequential Recommendation—0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
RAP: Runtime-Adaptive Pruning for LLM Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified