Unverified50% confidenceFactExact time
Standard agent reinforcement learning mainly relies on a single scalar reward at the endpoint, and the supervision signals for intermediate steps of the trajectory are wasted.
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10/15/2026
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UnverifiedIn multi-agent reinforcement learning, when opponents continuously improve, the training signal automatically remains in a difficulty range that is just challenging enough, avoiding the test-cramming phenomenon of fixed datasets.80% similarUnverifiedMulti-turn reinforcement learning uses the agent's multi-step interactions in a real environment as training signals, awarding rewards based on the quality of the final retrieval results.77% similarUnverifiedThe training signal of the value-objective method is essentially still the endpoint scalar reward, merely decomposed and distributed across different time steps, without introducing additional information.76% similarUnverifiedIn such tasks, reinforcement learning usually means having the model generate multiple candidate answers to the same question, then reinforcing high-scoring outputs and suppressing low-scoring ones based on a reward signal; crude reward design can induce score-gaming behavior.76% similarUnverifiedReinforcement learning in the post-training stage can improve answer accuracy on both in-distribution and out-of-distribution tasks, but does not consistently improve the validity of reasoning traces.74% similar
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