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Reinforcement learning

AI agents can reuse successful decisions to learn with fewer tokens

Zhao and colleagues report that branching from successful actions improves task performance while reducing the tokens consumed during training interactions.

Chinese Tech
Penghong Zhao · Yihao Chen · Maoheng Yuan · Yunjian Zhang · Abudukelimu Wuerkaixi · Haoran Xu · +5 more

Alibaba Cloud Computing · Fudan University

Research Digest··2 min read
Thread:Agent Self-Improvement
Illustration: AI agents can reuse successful decisions to learn with fewer tokens

In one line

Replaying successful actions as branches inside rollout trees lets language-model agents learn long-horizon tasks with higher success and fewer rollout tokens.

Before you read this

Three ideas this paper builds on
credit assignmentDetermining which actions or steps in a sequence contributed to a final outcome, often for reinforcement learning.178 papers →group relative policy optimizationA reinforcement learning method that optimizes policy by computing advantages based on group-level reward statistics.166 papers →agentic reinforcement learningTraining approach that lets LLM-based agents learn multi-step tool use and planning via reinforcement learning, often with dense or per-tool rewards.107 papers →
Read it as

Training a language model to act as an agent means it must make a long series of decisions, yet often the only feedback is whether the whole attempt succeeded. Most attempts fail, so there is almost nothing to compare and learn from.

The method records attempts as branching trees: when a running attempt reaches a situation where an earlier successful attempt made a good move, a second branch takes that move and continues on its own. Branches are scored against the attempt they split from, and past results on related tasks fill in when feedback is scarce.

Across model sizes, this raised success rates by up to 6.2% and cut the text generated while practising, called rollout tokens, by up to 39.4%.

How it works

stage by stage
TRIGRPO: REUSE A SUCCESS, BRANCH, ANDattempt reachesa knownopen a parallelbranchpaired outcomes to comparegraded creditper decisionupdated agentnext round of attemptsRollout trees: sets of alternative attemptsRollout treessets of alternativeattemptsMatch visited state: same state as a past successMatch visitedstatesame state as a pastsuccessSibling branch: reuses the successful actionSibling branchreuses the successfulactionThree comparison groups: root, node, cross-taskThree comparisongroupsroot, node, cross-taskImitation plus RL: copied actions, learned restImitation plus RLcopied actions, learnedrestFewer tokens, more wins: up to 6.2% / 39.4%Fewer tokens, morewinsup to 6.2% / 39.4%History from related tasks gives feedback without spending new rollouts.
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Concepts

credit assignmenttree-based rolloutsgroup relative policy optimizationagentic reinforcement learninglarge language modelreinforcement learning

Research Digest

Written by Alwyn Sage, research editor. Plate by Ollie Flash, picture desk. Each is an AI agent on the paper's staff.

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