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Alibaba Group has launched QwenLong-L1a brand new framework that permits massive language fashions (LLMs) to cause over extraordinarily lengthy inputs. This growth may unlock a brand new wave of enterprise purposes that require fashions to know and draw insights from in depth paperwork resembling detailed company filings, prolonged monetary statements, or complicated authorized contracts.
The problem of long-form reasoning for AI
Current advances in massive reasoning fashions (LRMs), significantly by means of reinforcement studying (RL), have considerably improved their problem-solving capabilities. Analysis exhibits that when educated with RL fine-tuning, LRMs purchase abilities just like human “sluggish pondering,” the place they develop subtle methods to sort out complicated duties.
Nonetheless, these enhancements are primarily seen when fashions work with comparatively quick items of textual content, usually round 4,000 tokens. The flexibility of those fashions to scale their reasoning to for much longer contexts (e.g., 120,000 tokens) stays a serious problem. Such long-form reasoning requires a strong understanding of the whole context and the power to carry out multi-step evaluation. “This limitation poses a big barrier to sensible purposes requiring interplay with exterior information, resembling deep analysis, the place LRMs should accumulate and course of data from knowledge-intensive environments,” the builders of QwenLong-L1 write of their paper.
The researchers formalize these challenges into the idea of “long-context reasoning RL.” In contrast to short-context reasoning, which frequently depends on information already saved inside the mannequin, long-context reasoning RL requires fashions to retrieve and floor related data from prolonged inputs precisely. Solely then can they generate chains of reasoning based mostly on this integrated data.
Coaching fashions for this by means of RL is difficult and sometimes ends in inefficient studying and unstable optimization processes. Fashions wrestle to converge on good options or lose their skill to discover numerous reasoning paths.
QwenLong-L1: A multi-stage strategy
QwenLong-L1 is a reinforcement studying framework designed to assist LRMs transition from proficiency with quick texts to sturdy generalization throughout lengthy contexts. The framework enhances present short-context LRMs by means of a rigorously structured, multi-stage course of:
Heat-up Supervised Nice-Tuning (SFT): The mannequin first undergoes an SFT part, the place it’s educated on examples of long-context reasoning. This stage establishes a stable basis, enabling the mannequin to floor data precisely from lengthy inputs. It helps develop elementary capabilities in understanding context, producing logical reasoning chains, and extracting solutions.
Curriculum-Guided Phased RL: At this stage, the mannequin is educated by means of a number of phases, with the goal size of the enter paperwork regularly growing. This systematic, step-by-step strategy helps the mannequin stably adapt its reasoning methods from shorter to progressively longer contexts. It avoids the instability usually seen when fashions are abruptly educated on very lengthy texts.
Problem-Conscious Retrospective Sampling: The ultimate coaching stage incorporates difficult examples from the previous coaching phases, guaranteeing the mannequin continues to study from the toughest issues. This prioritizes tough situations and encourages the mannequin to discover extra numerous and complicated reasoning paths.
QwenLong-L1 course of Supply: arXiv
Past this structured coaching, QwenLong-L1 additionally makes use of a definite reward system. Whereas coaching for short-context reasoning duties usually depends on strict rule-based rewards (e.g., an accurate reply in a math drawback), QwenLong-L1 employs a hybrid reward mechanism. This combines rule-based verification, which ensures precision by checking for strict adherence to correctness standards, with an “LLM-as-a-judge.” This choose mannequin compares the semanticity of the generated reply with the bottom reality, permitting for extra flexibility and higher dealing with of the varied methods appropriate solutions may be expressed when coping with lengthy, nuanced paperwork.
Placing QwenLong-L1 to the check
The Alibaba crew evaluated QwenLong-L1 utilizing doc question-answering (DocQA) as the first job. This situation is extremely related to enterprise wants, the place AI should perceive dense paperwork to reply complicated questions.
Experimental outcomes throughout seven long-context DocQA benchmarks confirmed QwenLong-L1’s capabilities. Notably, the QWENLONG-L1-32B mannequin (based mostly on DeepSeek-R1-Distill-Qwen-32B) achieved efficiency akin to Anthropic’s Claude-3.7 Sonnet Pondering, and outperformed fashions like OpenAI’s o3-mini and Qwen3-235B-A22B. The smaller QWENLONG-L1-14B mannequin additionally outperformed Google’s Gemini 2.0 Flash Pondering and Qwen3-32B.
Supply: arXiv
An necessary discovering related to real-world purposes is how RL coaching ends in the mannequin creating specialised long-context reasoning behaviors. The paper notes that fashions educated with QwenLong-L1 turn into higher at “grounding” (linking solutions to particular components of a doc), “subgoal setting” (breaking down complicated questions), “backtracking” (recognizing and correcting their very own errors mid-reasoning), and “verification” (double-checking their solutions).
As an example, whereas a base mannequin may get sidetracked by irrelevant particulars in a monetary doc or get caught in a loop of over-analyzing unrelated data, the QwenLong-L1 educated mannequin demonstrated a capability to interact in efficient self-reflection. It may efficiently filter out these distractor particulars, backtrack from incorrect paths, and arrive on the appropriate reply.
Methods like QwenLong-L1 may considerably develop the utility of AI within the enterprise. Potential purposes embody authorized tech (analyzing hundreds of pages of authorized paperwork), finance (deep analysis on annual studies and monetary filings for danger evaluation or funding alternatives) and customer support (analyzing lengthy buyer interplay histories to supply extra knowledgeable help). The researchers have launched the code for the QwenLong-L1 recipe and the weights for the educated fashions.
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