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Open problems in Recursive self improvement

Gradient conflicts in multi-teacher distillation1 paper raises it · 1 addresses it
Conflicting teacher signals degrade joint optimization and cause capability loss when training a single student from multiple teachers.
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillationraises · addresses
Unreliable supervision in cross-tokenizer distillation1 paper raises it · 1 addresses it
Vocabulary mismatch and alignment coverage issues make teacher feedback noisy or harmful for student training.
- Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliabilityraises · addresses
Inconsistent policy shifts during teacher training1 paper raises it · 1 addresses it
Endpoint policy shifts may hide early and late preference changes that cancel out, leading to poor transfer.
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillationraises · addresses
Lack of decision-grounded trajectory supervision1 paper raises it · 1 addresses it
Successful trajectories often include actions unsupported by the agent's visible history, reducing learning effectiveness.
- Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agentsraises · addresses
Credit assignment in long-horizon agent training1 paper raises it · 1 addresses it
Critic values vary across turns, making fine-grained credit allocation and policy updates difficult.
- Multiscale Policy Optimization for Long-Horizon LLM Agentsraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

recursive self improvement · ai · agent551 papers45 this week
agent collaboration · multi agent · based multi agent231 papers37 this week
scientific agents · research agents · ai agents214 papers29 this week
teacher policy distillation · policy distillation opd · teacher supervision375 papers26 this week
conversational agents · user simulators · user simulation185 papers22 this week
learning verifiable rewards · reinforcement learning verifiable · reinforcement learning rl172 papers20 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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All papers in Research

arXiv cs.AIOct 23
LeafSkill: Efficient Leaf-Level Action Grounding for Recursive LLM Agents
Shen Liu, Ze Zhou, Zhenguo Xu, Shaopu Wang, Yike Gao +1
Recursive large language model (LLM) agents solve long-horizon tasks through decomposition, execution, and environmental feedback. Yet after producing an atomic subtask, they often invoke the same large model again to translate a familiar instruction into an executable action. We observe that high-level planning is task-specific, whereas leaf-level action grounding recurs across tasks and can be checked against environment-admitted actions. We introduce LeafSkill, an external reuse layer that retrieves semantically related grounding experience, binds action templates to current objects and admissible actions, and uses a lightweight 1.7B selector to choose a bound action or defer the unchanged subtask to the original agent. Skills remain outside the model parameters, allowing a frozen selector to use post-training additions. Under lexical and syntactic paraphrases, exact matching retrieves none of the reference actions, while semantic retrieval preserves 96.0% and 97.0% candidate coverage; the frozen selector adopts 97.34% of unseen skill types. On an independent confirmation set of 200 Robotouille tasks, LeafSkill completes 137 tasks versus 121 for ReCAP (68.5% vs. 60.5%, exact…
PDF arXiv
arXiv cs.AIOct 23
Adaptive Control of Scientific Instruments with Nonstationary Responses through Multimodal Agents
Guoyang Shi, Hongwen Li, Yiding Yu, Zhuang Fu, Siqi Ding +4
Controlling scientific instruments requires parameters to be adjusted progressively in response to feedback from successive operations. When unobserved operating conditions evolve with operational history, responses become nonstationary, and a static mapping from actions to responses becomes inadequate for decisions across shots. Shot-to-shot tuning of neutral beam injection (NBI) ion sources in magnetic confinement fusion devices is representative of this class of problems: the conditions of electrode surfaces and vacuum are difficult to observe directly, discharge outcomes vary between shots, and experiments are costly and carry operational risks. We propose a physically grounded multimodal agent with three independently auditable components. Deterministic algorithms extract symbolic physical quantities and visual evidence from multichannel waveforms; a memory with two layers organizes trajectories from the current task, experience from earlier tasks, and procedural knowledge, from which a multimodal large language model (MLLM) generates candidate actions; and an independent action feasibility check enforces action constraints before execution. In a strictly chronological…
PDF arXiv
arXiv cs.CLOct 23
Beyond Final Outcomes: Quantifying Claim-Level Information Dynamics in Multi-Agent LLM Interaction
Haocheng Jiang, Herun Wan, Yongyuan Peng, Minnan Luo
When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task outcomes. We study such interaction as claim-level information dynamics and propose RIVER, which decomposes agent outputs into atomic claims and evaluates three complementary dimensions: convergence, exploration, and retention. Convergence captures cross-agent claim correspondence and stance alignment; exploration captures semantic breadth and extension beyond the source; and retention tracks source preservation and the persistence of new claims across stages and lineages. We evaluate five interaction strategies on 40 controlled news-generation cases. Repeatedly pooled interaction promotes convergence but narrows the information space, whereas distributed relay yields broader, less shared information. Further comparisons show that method-specific constraints increase exploration while weakening convergence and retention. Our findings suggest that collective information dynamics are better explained by underlying information-flow mechanisms than by simple downstream performance.
PDF arXiv
arXiv cs.AIOct 23
TGR-EvoDAG: A DAG-based Multi-Agent Code Evolution Framework for Generative Recommendation on Tencent
Junlin He, Junzhang Zhu, Yudong Li, Yihong Tang, Xuefeng Jiang +3
Large Language Model (LLM) agents have enabled automated code evolution for algorithm discovery. However, current recursive self-improvement (RSI) research for sequential recommendation faces a dilemma: general-purpose frameworks lack domain-specific feedback, while recommender-specific frameworks rely on linear evolution. Crucially, naively integrating them fails when applied to Generative Recommendation (GR). Unlike end-to-end models, GR relies on a module-cascaded pipeline (tokenization, pre-training, and alignment). In this paradigm, existing evolution methods suffer from: i) cost blindness, squandering budgets by ignoring stage-asymmetric retraining overheads; and ii) short-sighted evaluation, discarding promising upstream innovations that temporarily regress metrics before downstream adaptation. To bridge these gaps, we propose TGR-EvoDAG, a multi-agent code evolution framework tailored for GR. TGR-EvoDAG models code evolution as a Directed Acyclic Graph (DAG) to maintain multiple evolution branches and merge complementary innovations, unifying advanced search with rich GR diagnostic feedback. We further introduce stage-aware cost planning to optimize GPU-hour budgets, and…
PDF arXiv
arXiv cs.AIOct 23
Solvent: Long-Horizon Reasoning for Multi-Step Retrosynthesis
Yifei Wang, Botian Wang, YiFei Wang, Jiangtao Feng, Xinbo Zhang +6
Multi-step retrosynthesis requires recursively transforming a target molecule through successive reaction steps into purchasable building blocks, and recent work has begun to draw on large language model (LLM) reasoning to tackle the task. In its latest evolution, LLM reasoning has entered the long-horizon regime: models pursue objectives across interdependent steps, interact with environments, and adapt to feedback. This shift has markedly advanced LLM capabilities, as illustrated by breakthroughs in AI-driven mathematical research. At this frontier, we introduce Solvent, an 8B LLM that successfully extends long-horizon reasoning to multi-step retrosynthesis. Through a two-round post-training curriculum, Solvent learns to interleave iterative synthesis planning, external availability verification, and adaptive route revision. Experiments show that, following a $2.1\times$ performance boost from RL, Solvent scores 61.86% across four benchmarks, surpassing mid-scale language models and prior search-based retrosynthesis systems, and rivaling frontier trillion-parameter LLMs.
PDF arXiv
arXiv cs.AICodeOct 23▲ 48
RSIHub: A Composable Framework for Agent-Evolution Methods
Zimu Wang, Xiaobo Wang, Siyu Ye, Jiaqi Li, Silin Chen +9
Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery. We present RSIHub, an open-source framework for building, comparing, and extending these methods on shared infrastructure. Recipes compose reusable operators and declare mutable resources; RSIHub supplies execution, evaluation, recovery, and lineage. Researchers can focus on evolution policy while evaluation stays outside the mutable surface and results remain tied to candidates and evaluation conditions. We instantiate AHE, Hyperagents, A-Evolve, and GEPA without modifying the framework core, keeping method-specific logic in recipes and operators. We evaluate them with MiniSWE and Codex on Terminal Bench 2 and $τ^3$ Banking under fixed model and evaluator configurations. The largest observed overall pass@1 gain is 13.5 percentage points, while method rankings vary across settings and optimization gains do not always transfer to held-out tasks. RSIHub provides a common protocol for comparing evolution policies, inspectable candidate histories, and reusable components for developing new methods.
PDF arXiv Code

Papers

Topics in this field

recursive self improvement · ai · agent551 papers45 this week
agent collaboration · multi agent · based multi agent231 papers37 this week
scientific agents · research agents · ai agents214 papers29 this week
teacher policy distillation · policy distillation opd · teacher supervision375 papers26 this week
conversational agents · user simulators · user simulation185 papers22 this week
learning verifiable rewards · reinforcement learning verifiable · reinforcement learning rl172 papers20 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AIOct 23
LeafSkill: Efficient Leaf-Level Action Grounding for Recursive LLM Agents
Shen Liu, Ze Zhou, Zhenguo Xu, Shaopu Wang, Yike Gao +1
Recursive large language model (LLM) agents solve long-horizon tasks through decomposition, execution, and environmental feedback. Yet after producing an atomic subtask, they often invoke the same large model again to translate a familiar instruction into an executable action. We observe that high-level planning is task-specific, whereas leaf-level action grounding recurs across tasks and can be checked against environment-admitted actions. We introduce LeafSkill, an external reuse layer that retrieves semantically related grounding experience, binds action templates to current objects and admissible actions, and uses a lightweight 1.7B selector to choose a bound action or defer the unchanged subtask to the original agent. Skills remain outside the model parameters, allowing a frozen selector to use post-training additions. Under lexical and syntactic paraphrases, exact matching retrieves none of the reference actions, while semantic retrieval preserves 96.0% and 97.0% candidate coverage; the frozen selector adopts 97.34% of unseen skill types. On an independent confirmation set of 200 Robotouille tasks, LeafSkill completes 137 tasks versus 121 for ReCAP (68.5% vs. 60.5%, exact…
PDF arXiv
arXiv cs.AIOct 23
Adaptive Control of Scientific Instruments with Nonstationary Responses through Multimodal Agents
Guoyang Shi, Hongwen Li, Yiding Yu, Zhuang Fu, Siqi Ding +4
Controlling scientific instruments requires parameters to be adjusted progressively in response to feedback from successive operations. When unobserved operating conditions evolve with operational history, responses become nonstationary, and a static mapping from actions to responses becomes inadequate for decisions across shots. Shot-to-shot tuning of neutral beam injection (NBI) ion sources in magnetic confinement fusion devices is representative of this class of problems: the conditions of electrode surfaces and vacuum are difficult to observe directly, discharge outcomes vary between shots, and experiments are costly and carry operational risks. We propose a physically grounded multimodal agent with three independently auditable components. Deterministic algorithms extract symbolic physical quantities and visual evidence from multichannel waveforms; a memory with two layers organizes trajectories from the current task, experience from earlier tasks, and procedural knowledge, from which a multimodal large language model (MLLM) generates candidate actions; and an independent action feasibility check enforces action constraints before execution. In a strictly chronological…
PDF arXiv
arXiv cs.CLOct 23
Beyond Final Outcomes: Quantifying Claim-Level Information Dynamics in Multi-Agent LLM Interaction
Haocheng Jiang, Herun Wan, Yongyuan Peng, Minnan Luo
When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task outcomes. We study such interaction as claim-level information dynamics and propose RIVER, which decomposes agent outputs into atomic claims and evaluates three complementary dimensions: convergence, exploration, and retention. Convergence captures cross-agent claim correspondence and stance alignment; exploration captures semantic breadth and extension beyond the source; and retention tracks source preservation and the persistence of new claims across stages and lineages. We evaluate five interaction strategies on 40 controlled news-generation cases. Repeatedly pooled interaction promotes convergence but narrows the information space, whereas distributed relay yields broader, less shared information. Further comparisons show that method-specific constraints increase exploration while weakening convergence and retention. Our findings suggest that collective information dynamics are better explained by underlying information-flow mechanisms than by simple downstream performance.
PDF arXiv
arXiv cs.AIOct 23
TGR-EvoDAG: A DAG-based Multi-Agent Code Evolution Framework for Generative Recommendation on Tencent
Junlin He, Junzhang Zhu, Yudong Li, Yihong Tang, Xuefeng Jiang +3
Large Language Model (LLM) agents have enabled automated code evolution for algorithm discovery. However, current recursive self-improvement (RSI) research for sequential recommendation faces a dilemma: general-purpose frameworks lack domain-specific feedback, while recommender-specific frameworks rely on linear evolution. Crucially, naively integrating them fails when applied to Generative Recommendation (GR). Unlike end-to-end models, GR relies on a module-cascaded pipeline (tokenization, pre-training, and alignment). In this paradigm, existing evolution methods suffer from: i) cost blindness, squandering budgets by ignoring stage-asymmetric retraining overheads; and ii) short-sighted evaluation, discarding promising upstream innovations that temporarily regress metrics before downstream adaptation. To bridge these gaps, we propose TGR-EvoDAG, a multi-agent code evolution framework tailored for GR. TGR-EvoDAG models code evolution as a Directed Acyclic Graph (DAG) to maintain multiple evolution branches and merge complementary innovations, unifying advanced search with rich GR diagnostic feedback. We further introduce stage-aware cost planning to optimize GPU-hour budgets, and…
PDF arXiv
arXiv cs.AIOct 23
Solvent: Long-Horizon Reasoning for Multi-Step Retrosynthesis
Yifei Wang, Botian Wang, YiFei Wang, Jiangtao Feng, Xinbo Zhang +6
Multi-step retrosynthesis requires recursively transforming a target molecule through successive reaction steps into purchasable building blocks, and recent work has begun to draw on large language model (LLM) reasoning to tackle the task. In its latest evolution, LLM reasoning has entered the long-horizon regime: models pursue objectives across interdependent steps, interact with environments, and adapt to feedback. This shift has markedly advanced LLM capabilities, as illustrated by breakthroughs in AI-driven mathematical research. At this frontier, we introduce Solvent, an 8B LLM that successfully extends long-horizon reasoning to multi-step retrosynthesis. Through a two-round post-training curriculum, Solvent learns to interleave iterative synthesis planning, external availability verification, and adaptive route revision. Experiments show that, following a $2.1\times$ performance boost from RL, Solvent scores 61.86% across four benchmarks, surpassing mid-scale language models and prior search-based retrosynthesis systems, and rivaling frontier trillion-parameter LLMs.
PDF arXiv
arXiv cs.AICodeOct 23▲ 48
RSIHub: A Composable Framework for Agent-Evolution Methods
Zimu Wang, Xiaobo Wang, Siyu Ye, Jiaqi Li, Silin Chen +9
Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery. We present RSIHub, an open-source framework for building, comparing, and extending these methods on shared infrastructure. Recipes compose reusable operators and declare mutable resources; RSIHub supplies execution, evaluation, recovery, and lineage. Researchers can focus on evolution policy while evaluation stays outside the mutable surface and results remain tied to candidates and evaluation conditions. We instantiate AHE, Hyperagents, A-Evolve, and GEPA without modifying the framework core, keeping method-specific logic in recipes and operators. We evaluate them with MiniSWE and Codex on Terminal Bench 2 and $τ^3$ Banking under fixed model and evaluator configurations. The largest observed overall pass@1 gain is 13.5 percentage points, while method rankings vary across settings and optimization gains do not always transfer to held-out tasks. RSIHub provides a common protocol for comparing evolution policies, inspectable candidate histories, and reusable components for developing new methods.
PDF arXiv Code

Activity · 12 weeks

Fields · AI · updated 34m ago

Recursive self improvement

Found by the topic model: recursive self improvement, teacher policy distillation, multi agent debate.

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Open problems in Recursive self improvement

Gradient conflicts in multi-teacher distillation1 paper raises it · 1 addresses it
Conflicting teacher signals degrade joint optimization and cause capability loss when training a single student from multiple teachers.
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillationraises · addresses
Unreliable supervision in cross-tokenizer distillation1 paper raises it · 1 addresses it
Vocabulary mismatch and alignment coverage issues make teacher feedback noisy or harmful for student training.
- Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliabilityraises · addresses
Inconsistent policy shifts during teacher training1 paper raises it · 1 addresses it
Endpoint policy shifts may hide early and late preference changes that cancel out, leading to poor transfer.
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillationraises · addresses
Lack of decision-grounded trajectory supervision1 paper raises it · 1 addresses it
Successful trajectories often include actions unsupported by the agent's visible history, reducing learning effectiveness.
- Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agentsraises · addresses
Credit assignment in long-horizon agent training1 paper raises it · 1 addresses it
Critic values vary across turns, making fine-grained credit allocation and policy updates difficult.
- Multiscale Policy Optimization for Long-Horizon LLM Agentsraises · addresses

Named by an AI from these papers' abstracts.