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AgentEvolver: Towards Efficient Self-Evolving Agent System

Yunpeng Zhai, Shuchang Tao, Cheng Chen, Anni Zou, Ziqian Chen, Qingxu Fu, Shinji Mai, Li Yu, Jiaji Deng, Zouying Cao, Zhaoyang Liu, Bolin Ding, Jingren Zhou

arXiv:2511.10395v1cs.LGcs.AIcs.CL

TL;DR

Developing LLM agents for novel environments is challenged by costly task construction and sample-inefficient RL. AgentEvolver uses LLM-guided self-questioning, navigation, and attribution; the paper reports that its framework systematically alleviates task scarcity, inefficient exploration, and low sample utilization.

  • Problem

    Developing LLM agents for novel environments remains challenging because tailored RL tasks are costly to construct and existing methods are sample-inefficient.

    Source: §1 · p. 2

  • Method

    AgentEvolver uses an LLM-guided self-training loop with self-questioning for task generation, self-navigating for experience-guided exploration, and self-attributing for fine-grained credit assignment.

    Source: §1 · p. 2

  • Results

    AgentEvolver is reported to systematically alleviate task scarcity, inefficient exploration, and low sample utilization.

    Source: §8 · p. 26

  • Takeaways & Limitations

    The framework is positioned as a reusable foundation for building adaptive, tool-augmented agents.

    Source: §8 · p. 26

    The stated interaction-sandbox setting specifies state and action spaces and transition dynamics but provides no predefined reward function or task objective.

    Source: §2 · p. 3

Abstract

from arXiv · show

Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in diverse environments. However, current approaches to developing such agents remain costly and inefficient, as they typically require manually constructed task datasets and reinforcement learning (RL) pipelines with extensive random exploration. These limitations lead to prohibitively high data-construction costs, low exploration efficiency, and poor sample utilization. To address these challenges, we present AgentEvolver, a self-evolving agent system that leverages the semantic understanding and reasoning capabilities of LLMs to drive autonomous agent learning. AgentEvolver introduces three synergistic mechanisms: (i) self-questioning, which enables curiosity-driven task generation in novel environments, reducing dependence on handcrafted datasets; (ii) self-navigating, which improves exploration efficiency through experience reuse and hybrid policy guidance; and (iii) self-attributing, which enhances sample efficiency by assigning differentiated rewards to trajectory states and actions based on their contribution. By integrating these mechanisms into a unified framework, AgentEvolver enables scalable, cost-effective, and continual improvement of agent capabilities. Preliminary experiments indicate that AgentEvolver achieves more efficient exploration, better sample utilization, and faster adaptation compared to traditional RL-based baselines.

1 Introduction

AgentEvolver proposes an LLM-guided self-training loop to address costly task construction and inefficient learning in novel environments. It combines autonomous task generation, experience-guided exploration, and fine-grained credit assignment with infrastructure for practical agent development.

Introduction

  • RL agent development in novel environments is costly because tailored task construction is labor-intensive and PPO- or GRPO-style training relies on expensive, redundant trajectory sampling.The introduction identifies both high task-construction costs and poor sample efficiency in long-horizon, tool-augmented settings.Source: §1 · p. 2
  • AgentEvolver uses self-questioning for task generation, self-navigating for experience-guided exploration, and self-attributing for fine-grained trajectory credit assignment.Self-questioning probes environment state-action spaces and functional boundaries; self-navigating reuses past experience through hybrid policy learning and trajectory guidance; self-attributing assigns differentiated rewards to intermediate states and actions.Source: §1 · p. 2
  • Together, the mechanisms form an LLM-guided self-training loop that targets task scarcity, inefficient exploration, and low sample utilization through direct environmental interaction.The framework shifts training initiative from human-engineered pipelines toward autonomous exploration, task generation, and performance refinement.Source: §1 · p. 2
  • AgentEvolver also provides standardized environment interfaces, a unified multi-turn Context Manager, veRL integration, and modular components for extension.These developer-oriented features support interaction with diverse environments, policy optimization and parameter updates, and secondary development.Source: §1 · p. 2

2 Problem Formulation

The formulation asks an LLM agent to improve toward an unknown target-task distribution without predefined tasks or rewards, using generated proxy objectives and learning signals in an open-ended sandbox.

Environment Formulation

  • The interaction sandbox defines observable states, executable actions, and transition dynamics but supplies no predefined task objective or reward function.The agent must generate its own objectives and learning signals to improve its policy.Source: §2 · p. 3

Target Task Space and Oracle Reward

  • The agent is evaluated on an unknown distribution of tasks, each defined by a desired goal state and a ground-truth reward used to train a goal-conditioned policy.The policy seeks to maximize expected return across target tasks, starting from the environment’s initial-state distribution.Source: §2 · p. 4

Proxy Objective of Self-Evolution

  • Self-questioning generates candidate solvable tasks and a training distribution designed to approximate the unknown target distribution through appropriate difficulty and diversity.The formulation maps the environment to proxy tasks because the target distribution is not known a priori.Source: §2 · p. 4
  • Self-attributing infers proxy rewards that provide finer-grained credit assignment than sparse environment rewards, enabling richer learning feedback without human intervention.Self-navigating guides the policy toward high-learning-value trajectories to improve exploration efficiency in multi-turn environments.Source: §2 · p. 4

3 Self-Questioning

Self-questioning turns exploration in unfamiliar environments into user-aligned, curated training tasks with reference solutions and proxy rewards. Its pipeline combines profile-guided curiosity, adaptive synthesis, task filtering, and reference-based evaluation to support continual agent improvement.

3 Self-Questioning

  • Self-questioning addresses scarce, costly, and low-quality training data by generating proxy tasks and rewards from exploration, enabling task-agent co-evolution.The section frames synthesized-task quality as important to policy improvement and transfer to target tasks.Source: §3 · pp. 4–5

3.1 Curiosity-Guided Exploration with Environment Profiles

  • Environment profiles summarize entities, attributes, and operations to guide high-temperature curiosity; breadth-first exploration precedes focused depth-first investigation of promising trajectories.Profiles help the agent avoid aimless exploration, while limiting decisions to recent observations helps prevent premature convergence on one behavior pattern.Source: §3.1 · pp. 5–6

3.2 Adaptive Task Synthesis

  • Adaptive synthesis combines explored trajectories with user preferences for task difficulty and style, then extracts complete reference solutions from the action-observation traces.Because tasks are generated after exploration, their solutions may exceed the agent’s current capabilities while remaining recoverable from the explored trajectory.Source: §3.2 · pp. 6–7

3.3 Task Curation and Distributional Hybrid

  • Task curation removes duplicates through lexical and semantic checks, verifies executability using reference solutions, and can mix proxy tasks with target-distribution samples when available.The hybrid distribution’s λ controls the contribution of proxy tasks.Source: §3.3 · p. 7

3.4 Synthetic Task Reward with LLM Judge

  • The LLM judge provides proxy rewards for long trajectories through relevance checks, graduated scoring, and comparison against reference solutions for essential-step coverage.Unrelated, hallucinated, or redundant steps can receive zero scores; continuous scores distinguish correct solutions from incorrect attempts and reflect efficiency and intermediate errors.Source: §3.4 · p. 8

4 Self-Navigating

AgentEvolver’s self-navigating mechanism reuses prior trajectory knowledge to make exploration more structured while balancing experience guidance with flexibility. It acquires and incorporates natural-language experiences through retrieval, mixed rollouts, and optimization strategies designed to avoid over-reliance on prompts.

4 Self-Navigating

  • Self-navigating shifts exploration from unguided trial-and-error toward structured improvement by integrating experience reuse, trajectory guidance, and hybrid policy learning.The motivation is that conventional RL can produce redundant trajectories and slow convergence, while agents can apply knowledge from past failures to future tasks.Source: §4 · p. 8

4.1 Experience Acquisition

  • Experiences are natural-language guidance distilled from successful and failed trajectories, constructed offline and retrieved online for task-specific use.Pool construction preprocesses trajectories, extracts and validates insights, then indexes them; retrieval uses embedding similarity to select top-k experiences before re-ranking and rewriting them for relevance and adaptability.Source: §4 · pp. 8–9, §4.1 · pp. 9–10

4.2 Experience-mixed Rollout

  • Experience-mixed rollout interleaves unguided and experience-guided trajectories, with η controlling their balance to retain exploration while using prior knowledge.The method standardizes advantages across both trajectory types for consistent treatment during optimization.Source: §4.2 · p. 10

4.3 Experience Incorporation

  • Experience incorporation strips retrieved text before policy-gradient optimization and selectively boosts the upper clipping threshold for positive-advantage samples.Stripping reduces dependence on prompt cues, while boosting lets larger importance ratios pass without attenuation, preserving favorable experience-conditioned learning signals despite the rollout–optimization mismatch.Source: §4.3 · pp. 10–11

5 Self-Attributing

Self-attributing replaces sparse trajectory-level credit assignment with LLM-based judgments of each action’s contribution. It combines process-quality and outcome signals into token-level advantages for GRPO optimization.

5 Self-Attributing

  • AgentEvolver’s self-attributing mechanism addresses equal credit for all actions by using LLM reasoning to assess their contributions and improve fine-grained learning signals.It reframes credit assignment from temporal signal propagation to contribution-based attribution, then combines process quality and trajectory success after standardizing each separately.Source: §5 · p. 11

5.1 Step-wise Attribution via LLM-based Reasoning

  • The step-wise attribution pipeline retrospectively evaluates completed trajectories to judge each action’s logical correctness in context.It presents the task, intermediate steps, and outcome together in one pass, enabling the LLM to account for inter-step dependencies.Source: §5.1 · p. 11, System Prompt for Step-wise Attribution · p. 12

System Prompt for Step-wise Attribution

  • Binary GOOD/BAD labels provide directional, semantically grounded process feedback without task-specific scoring schemes or precise reward magnitudes.The labels operationalize attribution and form the process-quality channel, which is later combined with outcome effectiveness.Source: System Prompt for Step-wise Attribution · p. 12

5.2 Attribution-Based Reward Construction

  • Each GOOD step receives +1 and each BAD step −1; normalization statistics are computed over trajectory-average rewards so longer trajectories do not dominate.The resulting attribution signal is standardized before integration with outcome-based rewards.Source: §5.2 · pp. 12–13

5.3 Composite Reward Construction

  • The composite reward combines separately normalized attribution and outcome channels, applying the outcome reward only at the trajectory’s terminal step.The weight α balances process correctness against final task success; a higher α can establish procedural foundations before being lowered for task-specific fine-tuning.Source: §5.3 · pp. 13–14

5.4 Policy Optimization

  • Step-level advantages are broadcast to all response tokens in each action, providing token-level signals for vanilla and experience-guided rollouts.The resulting optimization signal combines intermediate process evaluation with final task success across both rollout types.Source: §5.4 · p. 14

6 Framework and Infrastructure

AgentEvolver organizes its learning mechanisms in a modular, service-oriented training system and provides context-management and environment-service infrastructure for scalable, multi-turn agent interaction.

6.1 Training Framework

  • A four-stage orchestration loop connects task synthesis, parallel trajectory rollout, experience summarization, and sample construction with model optimization.Hierarchical service, rollout-worker, and manager layers support multi-turn execution, while decoupled services and replaceable modules let researchers vary agent workflows and training components without changing the runtime interface.Source: §6.1 · pp. 14–16

6.2 Context Manager

  • The Context Manager unifies causal and editable rollouts through a mutable Live Context Timeline and an immutable Timeline Snapshot Recorder.The Recorder preserves token-level episode snapshots and merges redundant sequences with aligned loss masks, while the Timeline serves as editable short-term context.Source: §6.2 · pp. 16–17

6.2.1 Rollout Paradigms and Context-Managing Templates

  • Four shared LCT–TSR templates span efficient causal rollouts, structured reasoning, long-horizon windowing, and agent-controlled context management.The causal template retains only the final snapshot; reasoning-augmented prompts encourage thinking before action; sliding windows summarize older content to keep GPU memory constant; and self-management lets the agent preserve, delete, or compress messages.Source: §6.2.1 · p. 17

6.3 Environment Service: A Scalable and Gym-Compatible Execution Backend

  • The Gym-compatible Environment Service decouples execution from the training loop to provide remote, concurrent, isolated environment instances on demand.Ray actors support dynamic scaling across CPU and GPU resources and synchronous or asynchronous interaction modes.Source: §6.3 · pp. 17–18

7 Experiments · 7.1 Experimental Settings

AgentEvolver is evaluated on AppWorld and BFCL v3 using Qwen2.5 policy backbones and a Vanilla GRPO baseline. The settings define benchmark-specific evaluation metrics and configure GRPO training and the system’s self-questioning, self-navigating, and self-attributing mechanisms.

7 Experiments

  • The experiments are organized around experimental settings, main results across tasks and configurations, and ablation and analytical studies of framework modules.The supplied passages describe the settings but do not provide the main-results or ablation findings.Source: §7 · p. 18

7.1.1 Benchmarks and Evaluations

  • AgentEvolver is evaluated on AppWorld and the multi-turn split of BFCL v3, two long-horizon tool-use benchmarks with sparse terminal rewards.Both are served through an Environment Service with Ray-based isolated actors and a unified HTTP interface.Source: §7.1.1 · p. 18
  • AppWorld uses Task Goal Completion (TGC), while BFCL v3 requires both state-based and response-based checks; reported metrics are avg@8 and best@8 over 8 rollouts.Force-terminated runs count as incorrect, and trajectories are capped at 30 steps.Source: §7.1.1 · p. 18

7.1.2 Baselines and Backbone Models

  • The experiments use Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct as policy networks and compare against Vanilla GRPO.The baseline uses only the final sparse outcome signal, without AgentEvolver’s experience-based guidance or process-based rewards.Source: §7.1.2 · p. 18

7.1.3 Implementation Details

  • Policies are trained with a GRPO-style method using a learning rate of 1 × 10−6, batch size 32, 40 epochs per update, and KL penalty 0.001.Experiments use 8 NVIDIA A100 (80GB) GPUs and PyTorch with veRL.Source: §7.1.3 · p. 18
  • Self-Questioning synthesizes hard queries from AppWorld and BFCL environment profiles and user preferences, with benchmark-specific exploration steps.Queries involve two entities, three attributes, and three operations; Nb and Nd are 3 and 17 for AppWorld and 3 and 27 for BFCL. Qwen-Plus handles exploration and task synthesis, Qwen3-235B-A22B judges queries, lexical deduplication uses a 0.8 threshold, and ptrain advantages are downscaled by 0.5.Source: §7.1.3 · pp. 18–19
  • Self-Navigating uses four experience-acquisition tasks, retrieves the top five experiences, and mixes experience-guided trajectories at η = 0.5.The cold-start pool uses self-questioning tasks; importance-sampling ratios are clipped at 0.28 with an extended upper bound of 0.6. Ablations report avg@4 and best@4 without experience guidance at test time.Source: §7.1.3 · p. 19
  • Self-Attributing uses Qwen-Max to jointly assess trajectory steps, maps attribution labels to signed unit scores, and normalizes them within groups before fusion with outcome signals.The outcome weight β is fixed at 1, α controls attribution weight, and near-zero-variance signals are neutralized for stability.Source: §7.1.3 · p. 19

7.2 Main Results

AgentEvolver combines self-questioning, self-navigating, and self-attributing; the full system improves performance across benchmarks, while component analyses show gains from the individual mechanisms.

7.2 Main Results

  • AgentEvolver overall improves 7B overall avg@8 by 29.4% and best@8 by 36.1%, and 14B overall avg@8 by 27.8% and best@8 by 30.3%.For 7B, avg@8 gains are AppWorld +30.6% and BFCL v3 +28.1%, and best@8 gains are AppWorld +45.6% and BFCL v3 +26.6%. For 14B, avg@8 gains are AppWorld +30.7% and BFCL v3 +24.9%, and best@8 gains are AppWorld +38.0% and BFCL v3 +22.6%.Source: §7.2 · p. 19
  • Self-questioning raises avg@8 from 15.8% to 36.1% for 7B and from 29.8% to 52.3% for 14B by autonomously generating diverse training tasks.The passage describes this as mitigating task scarcity and establishing a strong baseline.Source: §7.2 · p. 20
  • Adding self-navigating raises avg@8 to 39.8% (7B) and 54.1% (14B); self-attributing instead raises it to 41.3% and 56.4%, respectively.The passage attributes these gains to more efficient exploration and fine-grained credit assignment, and says the complete system achieves the highest performance across all settings as the three mechanisms complement one another.Source: §7.2 · p. 20

7.3 Results of Self-Questioning

Section 7.3 evaluates self-questioning synthetic data through comparisons with target-task data, data-volume and cross-domain tests, and an ablation of the LLM Judge. The findings address training effectiveness, data efficiency, generalization, and the judge’s role in producing useful synthetic rewards.

7.3.1 Effectiveness of Synthetic Data

  • On Appworld and BFCL, synthetic-data training improves over zero-shot and approaches original-data performance, while hybrid data surpasses original-data training.The results suggest that self-questioning generates diverse, useful questions that can guide capability improvement automatically and expand beyond the original data.Source: Table 2 · p. 20, §7.3.1 · p. 20

7.3.2 Influence of Synthetic Data Quantity

  • 40.3% performance is achieved with 100 synthetic samples; performance rises at 200 and 500 samples, with diminishing gains as volume increases.The authors attribute efficiency to diverse exploratory tasks and argue inexpensive synthetic data can reduce reliance on costly data while enabling scaling.Source: §7.3.2 · p. 20, Figure 12 · p. 21

7.3.4 Ablation Study on the LLM Judge

  • The LLM Judge’s basic scoring principle improves performance over a naive judge, and adding an exploration-phase reference solution yields substantial further improvement.The reference solution provides an environment-specific scoring standard that can generalize across environments without high-quality human annotation, bringing results close to human-curated-data training.Source: Figure 12 · p. 21, §7.3.4 · p. 21

7.3.3 Capability Generalization with Synthetic Data

  • The Qwen2.5-14B model transferred from Appworld synthetic-data training to BFCL shows only a 4.3% performance drop, indicating cross-domain capability generalization.The authors attribute improvement to both general and domain-specific capability gains.Source: Table 3 · p. 21, §7.3.3 · p. 21

7.4 Results of Self-Navigating

Self-navigating uses retrieved experience to improve trajectories, then balances experience-guided exploitation with exploration so training can internalize useful behavior and sustain long-term gains.

7.4.1 Effectiveness of Experience on Navigation

  • ↑5.4% avg@4 and ↑6.7% best@4 average gains on AppWorld and BFCL show that retrieved experience improves trajectory quality even without additional training.Experience stabilizes rollouts and raises their performance ceiling by providing navigation signals during inference.Source: Figure 13 · p. 22, §7.4.1 · p. 22

7.4.2 Implicit vs. Explicit Experience Learning

  • Implicit experience learning exceeds ICL-only by ↑34.2% in avg@4 and ↑28.1% in best@4, and vanilla RL by ↑7.9% in avg@4 and ↑8.5% in best@4.It internalizes experience through RL and performs without retrieval at inference; removing selective boosting drops below the RL baseline by ↓0.5% in avg@4 and ↓2.9% in best@4.Source: Table 4 · p. 22, §7.4.2 · p. 23

7.4.3 Exploration vs. Exploitation Dynamics

  • η = 0.5 best balances experience-guided exploitation and exploration during training, while higher experience proportions improve immediate inference rewards but weaken long-term training outcomes.Larger η accelerates early training gains but ultimately suppresses exploration and sustainable performance.Source: Figure 13 · p. 22, §7.4.3 · p. 23

7.4.4 Short-term vs. Long-term Optimization

  • ˆϵhigh = 0.6 best balances rapid early progress and stable long-term optimization; larger values speed learning within 20 steps but harm performance by 80 steps through overfitting.The parameter relaxes the GRPO clipping threshold to preserve positive samples that experience-guided distribution shifts might otherwise discard.Source: §7.4.4 · p. 23, Figure 14 · p. 23

7.5 Results of Self-Attributing

Section 7.5 evaluates self-attributing on AppWorld and BFCL v3 using synthetic self-questioning data, examining its effectiveness, sample efficiency, and sensitivity to the attribution weight α.

7.5.1 Effectiveness of Self-Attributing

  • Attributing achieves 47.6% avg@8 macro-average on 7B and 62.0% avg@8 plus 75.7% best@8 on 14B; AppWorld avg@8 rises from 3.1% to 38.4% and 17.8% to 59.2%.The AppWorld gains are 35.3 and 41.4 percentage points, respectively.Source: Table 5 · p. 24, §7.5.1 · p. 24
  • Combining attribution and outcome rewards performs best: attribution-only feedback underperforms outcome-only feedback, while outcome-only rewards lack step-wise credit assignment.Attribution supplies intermediate guidance, but terminal outcomes are needed to ground learning in task objectives; together, the signals support reasoning quality and task success.Source: §7.5.1 · p. 24

7.5.2 Sample Efficiency of Self-Attributing

  • Attributing reaches 90% of baseline final performance in 40 versus 90 steps on AppWorld and 20 versus 60 on BFCL v3, with AUCs of 46.26 versus 41.03 and 61.02 versus 55.78, respectively.The reported step reductions are 55% on AppWorld and 67% on BFCL v3.Source: Figure 15 · p. 25, Table 6 · p. 25, §7.5.2 · p. 25

7.5.3 Hyperparameter Analysis

  • On AppWorld, α=0.30 reaches 45% at 20 steps versus the baseline’s 28%, but ends at 43%; α=0.10 and 0.20 converge to approximately 59%, above the baseline’s 55%.α=0.05 has slower early gains but comparable final performance; the reported preferred range is α∈[0.10, 0.20].Source: Figure 16 · p. 25, §7.5.3 · pp. 25–26

7.5.2 Sample Efficiency of Self-Attributing

  • Dense step-wise attribution feedback complements sparse terminal rewards, enriching gradient information to reduce variance and redundant exploration.The analysis suggests excessive attribution weighting can overfit the LLM judge’s heuristic labels, while too little attribution slows early progress.Source: §7.5.2 · p. 25, §7.5.3 · p. 26

7.6 Effectiveness of Context-Managing Templates

The section evaluates four Context-Managing Templates on AppWorld with Qwen3-14B using TGC and Scenario Goal Completion metrics. SCMT achieves the best overall performance, particularly on the long-horizon TGC@8 metric.

7.6 Effectiveness of Context-Managing Templates

  • The evaluation compares four fundamental Context-Managing Templates on AppWorld using TGC and Scenario Goal Completion metrics, with Qwen3-14B as the policy backbone.Source: §7.6 · p. 26
  • SCMT achieves the best overall AppWorld performance, particularly on TGC@8, by compressing or discarding redundant context to focus reasoning and streamline agent–environment interactions.AppWorld requires reasoning over extensive tool API lists, of which only a small subset is relevant to each task.Source: Table 7 · p. 26, §7.6 · p. 26
  • RAT ranks second through explicit think-before-act steps; SWT performs moderately as summaries may omit long-sequence dependencies, while BCT is robust but scores lower in extended contexts.SWT has lower contextual consistency than SCMT, and BCT lacks adaptive memory control.Source: §7.6 · p. 26

8 Conclusion and Next Steps

AgentEvolver combines LLM-guided task generation, experience-guided exploration, and fine-grained credit assignment to address task scarcity, inefficient exploration, and low sample utilization. The conclusion also presents reusable infrastructure and outlines three directions for extending the system.

8 Conclusion and Next Steps

  • AgentEvolver combines self-questioning, self-navigating, and self-attributing to address task scarcity, inefficient exploration, and low sample utilization in RL-driven agent training.The framework moves learning initiative from fixed, human-engineered pipelines to LLM-guided improvement across tasks, trajectories, and policy.Source: §8 · p. 26
  • Its practical infrastructure standardizes environment interfaces, integrates with RL stacks such as veRL, and supports modular extension as a foundation for adaptive, tool-augmented agents.The authors position it as both a research vehicle and a reusable foundation.Source: §8 · p. 26

Next Steps

  • AgentEvolver will target more complex, higher-value environments, co-designing environments and self-questioning curricula to uncover functional boundaries and produce deployable models with measurable impact.Examples include multi-API enterprise workflows, safety-critical tool chains, and long-horizon interleaved objectives, building on validations in APPWORLD and BFCL.Source: Next Steps · p. 27
  • The authors will test AgentEvolver with larger LLM policy models, examining how capacity affects task quality, exploration redundancy, and credit assignment while studying compute–data trade-offs and cost-aware curricula.The proposed investigations focus on reasoning depth, reflective ability, experience reuse, and causal reasoning in long-horizon training.Source: Next Steps · p. 27
  • The authors aim to unify exploration, experience abstraction, self-attribution, and inference within one model or a tightly coupled model family for LLM-level self-improvement.The model would generate tasks and hypotheses, navigate with distilled priors, attribute outcomes to internal decisions, and update reasoning and control policies.Source: Next Steps · p. 27
  • The proposed directions will stress-test AgentEvolver in realistic, dynamically evolving settings, with larger models and end-to-end self-evolving loops expected to improve sample efficiency, robustness, and cross-environment generalization.The stated aim is scalable, cost-effective, continually improving agentic systems.Source: Next Steps · p. 27

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