Ssa Parallel Reasoning Sample Set Aggregator Scaling test‑time compute by sampling multiple reasoning paths yields large gains but leaves an oracle gap. we introduce ssa, a tiny llm fine‑tuned with grpo to read k candidate solutions and emit one final answer. In this paper, we propose a new way to leverage such multiple sample set. we train a compact llm, called sample set aggregator (ssa), that takes a concatenated sequence of multiple samples and output the final answer, optimizing it for the answer accuracy with reinforcement learning.
Ssa Parallel Reasoning Sample Set Aggregator It highlights the development of a novel test time scaling approach, sample set aggregator (ssa), which combines aspects of parallel and sequential scaling while optimizing output through reinforcement learning (rl). We train a compact llm, called sample set aggregator (ssa), that takes a concatenated sequence of multiple samples and output the final answer, optimizing it for the answer accuracy with reinforcement learning. Ple set. we train a compact llm, called sample set aggregator (ssa), that takes a concatenated sequence of multiple samples and output the final answer, optimizing it for the answer accuracy with reinforcement. Researchers from cuny, princeton, and nyu develop the sample set aggregator (ssa), a framework that uses a compact trainable llm to sequentially process and synthesize multiple parallel answers from a frozen base llm, achieving superior performance over existing test time scaling methods by training a small 0.5 3b parameter model with reinforcem.
Ssa Parallel Reasoning Sample Set Aggregator Ple set. we train a compact llm, called sample set aggregator (ssa), that takes a concatenated sequence of multiple samples and output the final answer, optimizing it for the answer accuracy with reinforcement. Researchers from cuny, princeton, and nyu develop the sample set aggregator (ssa), a framework that uses a compact trainable llm to sequentially process and synthesize multiple parallel answers from a frozen base llm, achieving superior performance over existing test time scaling methods by training a small 0.5 3b parameter model with reinforcem. This paper introduces a sample set aggregator (ssa), which represents a hybrid approach to enhancing large language model reasoning that bridges the gap between existing parallel and sequential scaling methods. In this paper, we propose a new way to leverage such multiple sample set. we train a compact llm, called sample set aggregator (ssa), that takes a concatenated sequence of multiple samples and output the final answer, optimizing it for the answer accuracy with reinforcement learning.
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