R$^2$ Flow is a recursive self‑improvement framework for large‑language‑model agents that alternates policy learning, independent verification, and versioned skill‑library updates on a shared‑state orchestration graph. By merging histories that differ only in the order of independent steps, it pools evidence across equivalent executions and uses flow‑share readouts, signed utility ranks, verifier evidence, and residual‑variance plateaus to decide when and which skills to edit. Experiments on question answering, mathematical reasoning, interactive decision making, and code generation show that R$^2$ Flow improves task accuracy and library‑edit precision compared to heuristic orchestration, reinforcement learning, and other skill‑evolution baselines, and it transfers across different executors.
arXiv:2607. 05297v1 Announce Type: new Abstract: Recent LLM agents tackle increasingly long-horizon, open-ended tasks, and external skills, reusable procedural knowledge supplied to the agent, further extend this capability.
SIPO (Self‑Instructing Policy Optimization) unifies reinforcement learning with on‑policy self‑distillation by using a contrastive self‑teacher to generate token‑level credit signals. The method samples multiple rollouts per prompt, pairs each with a reference answer and its mistakes, and uses the difference in teacher log‑probabilities to provide dense feedback while still respecting the overall task reward. Experiments on reasoning and code‑generation benchmarks show that SIPO outperforms both RLVR and OPSD baselines without requiring an external teacher or extra generation steps.
arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.
arXiv:2608. 03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions deserve credit.
The paper introduces MMLA‑RSI, a recursive self‑improvement framework within a Memory‑Mediated Learning Architecture. It presents two concrete implementations: Reasoning‑Time Training (RTT), which updates the policy state, and Reasoning‑Time Memory Adaptation (RTMA), which modifies the authoritative memory. Both routes treat their own adaptation mechanisms as improvable objects, enabling successive generations of agents to refine policy and memory through bounded rule languages and shared interfaces.
The paper introduces temporal self‑distillation for reinforcement learning with verifiable rewards (RLVR), proposing that a policy can learn from a stronger future checkpoint of itself. Two methods—Near‑Future Policy Optimization (NPO) and Near‑Future Policy Distillation (NPD)—use verified future‑self trajectories and token‑level transfer, respectively, while AutoNPO adaptively selects the optimal future checkpoint. Experiments on eight image‑text benchmarks show that near‑future teachers yield higher performance than far‑future ones, indicating that the balance between new capability and learner compatibility is key.
arXiv:2605.08693v3 Announce Type: replace Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and...
R$^2$Orch introduces a Recursive Self-Play Orchestration framework that uses an inner self‑play loop to improve an orchestrator on self‑selected tasks and an outer loop that refines how the inner loop improves. The method employs Recursive Learnability to reward task selection that yields diverse outcomes as skills are removed, and Skill Withdrawal to prune skills when the orchestrator performs equally well without them. Experiments on twelve datasets demonstrate that R$^2$Orch outperforms existing baselines in question answering, math, code, and interactive tasks.
arXiv:2608. 08677v1 Announce Type: new Abstract: Skill evolution improves agent skills through feedback over time, with failed trajectories often providing informative signals by revealing incomplete or misleading behaviors.
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
arXiv:2606. 20002v1 Announce Type: cross Abstract: This work presents a general framework for training large language models (LLMs) to "Connect the Dots" (CoD), a meta-capability required by long-lifecycle agents: as an LLM-based AI agent gets deployed in an environment, it solves a long sequence of tasks while continuously exploring the environment, learning from its own experiences, and iteratively self-updating its context about the environment, thereby achieving progressively better performance on future tasks conditioned on the updated context.
CounterRoute is an online reinforcement‑learning framework that jointly learns how to route a language model’s reasoning between a ‘think’ and a ‘direct answer’ mode, using a single shared policy derived from a dual‑mode checkpoint. It employs counterfactual rollouts to credit routing decisions and a curriculum that starts with forced dual‑mode rollouts before shifting to self‑routed updates, achieving better accuracy‑efficiency trade‑offs across nine benchmarks. The method reduces generated tokens by up to 51% on Qwen3‑8B while improving macro‑average accuracy, and its routing strategy generalizes to unseen coding, science, and commonsense tasks.