arXiv Machine Learning
Jul 7
RL Forgets! Towards Continual Policy Optimization
arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.
Continual Reasoning Gym: Diagnosing and Harnessing Shared Reasoning in Continual RLVR
The paper introduces Continual Reasoning Gym, a continual reinforcement learning with verifiable rewards (RLVR) environment that sequences text and visual reasoning tasks. It finds that sequential RLVR suffers modest forgetting and underperforms multitask RLVR, but that shared reasoning structures can be leveraged. The authors propose Continual Prompt Replay (CPR), which replays previous-task prompts and regenerates responses, achieving performance comparable to multitask RLVR.
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arXiv AI
Jun 19
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
arXiv:2510. 21978v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models.
arXiv Machine Learning
Jul 10
When Does Continual Learning Require Learning
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
arXiv Machine Learning
Jul 20
Rethinking Transfer in Continual Learning: A Replay-Based Realisation
arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.
arXiv Machine Learning
Jun 17
Continual Self-Improvement with Lightweight Experiential Latent Memories
arXiv:2606. 17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process.
arXiv AI
Jun 11
ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward
arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.
arXiv AI
Aug 20
Harness Continual Learning: Continual Adaptation Beyond Model Parameters
The paper introduces Harness Continual Learning (HCL), a paradigm where an agent’s state evolves through prompts, memories, tools, skills, and routing rules while keeping the underlying foundation model frozen. HCL defines harness-level forgetting and proposes a guarded evolution process involving a Continual Optimizer and Evaluator to ensure improvements without losing prior behavior. Experiments across textual reasoning, multimodal perception, and open‑world interaction show over 10% performance gains and demonstrate how the stability–plasticity trade‑off can be explicitly tuned.
Hugging Face Trending Papers
Jul 21
REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning
Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and agentic tool use in large language models (LLMs). However, continuing to scale it across vast task domains of interest remains challenging in both computational infrastructure and cost, especially when considering RL as merely a one-off learning stage.
arXiv AI
Jul 23
REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning
arXiv:2607. 19450v1 Announce Type: cross Abstract: Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and agentic tool use in large language models (LLMs).
arXiv AI
Jul 3
Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails
arXiv:2607. 02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined.
Hugging Face Trending Papers
Aug 19
Harness Continual Learning: Continual Adaptation Beyond Model Parameters
The paper introduces Harness Continual Learning (HCL), a paradigm where an agent’s state evolves through prompts, memories, tools, skills, and routing rules while keeping the foundation model frozen. HCL defines harness-level forgetting and proposes guarded harness evolution with a Continual Optimizer and Evaluator to balance improvement, retention, and validity. Experiments across textual reasoning, multimodal perception, and open‑world interaction show over 10% performance gains and demonstrate explicit control over the stability–plasticity trade‑off.
arXiv AI
Jun 12
ARROW: Augmented Replay for RObust World models
arXiv:2603. 11395v3 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks.